{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch_parametrized-curve"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch_parametrized-curve.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import sys\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.utils import shuffle\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n",
    "\n",
    "\n",
    "notebook.tqdm.pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainset saved.\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retention calculated.\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.876        4.2    4.20       4176\n",
      "          1,3,3           8.6          0.883        9.2    2.19       2711\n",
      "        1,3,3,3          18.2          0.858       14.0    1.52       1616\n",
      "      1,3,3,3,3          37.5          0.835       23.2    1.66        822\n",
      "    1,3,3,3,3,3          78.8          0.850       35.6    1.53        384\n",
      "  1,3,3,3,3,3,3         122.3          0.903       39.3    1.10        171\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6589\n",
      "          3,3,3           9.0          0.960       23.7    1.56       5162\n",
      "        3,3,3,3          19.4          0.942       44.2    1.86       3519\n",
      "      3,3,3,3,3          39.1          0.926       54.5    1.23       1922\n",
      "    3,3,3,3,3,3          76.6          0.930      106.8    1.96       1074\n",
      "  3,3,3,3,3,3,3         118.7          0.949      155.3    1.45        480\n",
      "3,3,3,3,3,3,3,3         131.1          0.970      617.2    3.97        100\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       38.9    3.21       7517\n",
      "          4,3,3          18.0          0.963       56.8    1.46       5303\n",
      "        4,3,3,3          33.3          0.952       84.3    1.48       3012\n",
      "      4,3,3,3,3          48.3          0.953      128.3    1.52       1353\n",
      "    4,3,3,3,3,3          67.3          0.958      112.9    0.88        496\n",
      "  4,3,3,3,3,3,3          77.6          0.978      113.2    1.00        244\n",
      "4,3,3,3,3,3,3,3         112.9          0.984      177.8    1.57        168\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[df['type'] != 3].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "df['i'] = 1\n",
    "df['r_history'] = \"\"\n",
    "df['t_history'] = \"\"\n",
    "col_idx = {key: i for i, key in enumerate(df.columns)}\n",
    "\n",
    "\n",
    "# code from https://github.com/L-M-Sherlock/anki_revlog_analysis/blob/main/revlog_analysis.py\n",
    "def get_feature(x):\n",
    "    last_kind = None\n",
    "    for idx, log in enumerate(x.itertuples()):\n",
    "        if last_kind is not None and last_kind in (1, 2) and log.type == 0:\n",
    "            return x.iloc[:idx]\n",
    "        last_kind = log.type\n",
    "        if idx == 0:\n",
    "            if log.type != 0:\n",
    "                return x.iloc[:idx]\n",
    "            x.iloc[idx, col_idx['delta_t']] = 0\n",
    "        if idx == x.shape[0] - 1:\n",
    "            break\n",
    "        x.iloc[idx + 1, col_idx['i']] = x.iloc[idx, col_idx['i']] + 1\n",
    "        x.iloc[idx + 1, col_idx['t_history']] = f\"{x.iloc[idx, col_idx['t_history']]},{x.iloc[idx, col_idx['delta_t']]}\"\n",
    "        x.iloc[idx + 1, col_idx['r_history']] = f\"{x.iloc[idx, col_idx['r_history']]},{x.iloc[idx, col_idx['r']]}\"\n",
    "    return x\n",
    "\n",
    "df = df.groupby('cid', as_index=False, group_keys=False).progress_apply(get_feature)\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df[\"t_history\"] = df[\"t_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df[\"r_history\"] = df[\"r_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "def cal_retention(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group['retention'] = round(group['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x]).mean(), 4)\n",
    "    group['total_cnt'] = group.shape[0]\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history', 'delta_t'], group_keys=False).progress_apply(cal_retention)\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2, 0.2, 0.2, 1, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "def next_interval(retention, staiblity, f):\n",
    "    return max(1, round(np.power(np.log(retention)/np.log(0.9), 1/f) * staiblity))\n",
    "\n",
    "def forgetting_curve(s, t, f):\n",
    "    return np.power(0.9, np.power(t / s, f))\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w, dtype=torch.float32))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "    \n",
    "    def exp_forgetting_curve(self, s, t):\n",
    "        return torch.pow(0.9, torch.pow((t / s).clamp(1/86400, 36500), self.w[13]))\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] - self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = self.exp_forgetting_curve(state[:,0], X[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.01, 0.2)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            w[13] = w[13].clamp(0.01, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from [self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist()]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=1, lr=1e-2, batch_size=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        self.eval()\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader))\n",
    "\n",
    "        for i, batch in enumerate(self.pre_train_data_loader):\n",
    "            self.model.train()\n",
    "            self.optimizer.zero_grad()\n",
    "            sequences, delta_ts, labels, seq_lens = batch\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences)\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = self.model.exp_forgetting_curve(stabilities, delta_ts)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "            self.model.apply(self.clipper)\n",
    "            pbar.update(n=real_batch_size)\n",
    "\n",
    "        pbar.close()\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "\n",
    "\n",
    "        for k in range(self.n_epoch):\n",
    "            self.eval()\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = self.model.exp_forgetting_curve(stabilities, delta_ts)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "                \n",
    "        self.eval()\n",
    "\n",
    "        w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "        return w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sampler = RevlogSampler(self.train_set, batch_size=4096)\n",
    "            train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "            loss = 0\n",
    "            for batch in train_data_loader:\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = self.model.exp_forgetting_curve(stabilities, delta_ts)\n",
    "                loss += self.loss_fn(retentions, labels)\n",
    "            self.avg_train_losses.append(loss/len(train_data_loader))\n",
    "            print(f\"Loss in trainset: {self.avg_train_losses[-1]:.4f}\")\n",
    "\n",
    "            sampler = RevlogSampler(self.test_set, batch_size=4096)\n",
    "            test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "            loss = 0\n",
    "            for batch in test_data_loader:\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = self.model.exp_forgetting_curve(stabilities, delta_ts)\n",
    "                loss += self.loss_fn(retentions, labels)\n",
    "            self.avg_eval_losses.append(loss/len(test_data_loader))\n",
    "            print(f\"Loss in testset: {self.avg_eval_losses[-1]:.4f}\")\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47f0867e550249f1b6f868e8bac18090",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/223795 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/fsrs4anki/lib/python3.8/site-packages/sklearn/model_selection/_split.py:885: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 147736 TEST: 76059\n",
      "dataset built\n",
      "Loss in trainset: 0.3519\n",
      "Loss in testset: 0.3179\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06b12f6bb6474f4e881c7f2dac3d4c6e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/17371 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.5837, 2.264, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0, 0.61]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d76dbf42b3684d7b98c4ff3c3757495d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/391095 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3426\n",
      "Loss in testset: 0.3077\n",
      "iteration: 39168\n",
      "w: [0.6671, 2.3561, 5.1148, 1.216, 1.233, 0.0164, 1.2283, 0.013, 0.8225, 1.6253, 0.6674, 0.6772, 0.9708, 0.4719]\n",
      "iteration: 78336\n",
      "w: [0.6671, 2.3561, 4.7685, 1.1497, 1.362, 0.0686, 1.1895, 0.073, 0.8615, 1.7214, 0.6115, 0.6595, 1.2338, 0.4154]\n",
      "iteration: 117504\n",
      "w: [0.6671, 2.3561, 4.7707, 1.245, 1.5406, 0.0106, 1.1813, 0.1103, 0.8977, 1.689, 0.6614, 0.7267, 1.3374, 0.5269]\n",
      "Loss in trainset: 0.3253\n",
      "Loss in testset: 0.2905\n",
      "iteration: 156477\n",
      "w: [0.6671, 2.3561, 4.6781, 1.4661, 1.4895, 0.0, 1.1712, 0.1143, 0.9116, 1.7687, 0.5747, 0.7598, 1.3962, 0.5431]\n",
      "iteration: 195645\n",
      "w: [0.6671, 2.3561, 4.6556, 1.3415, 1.6654, 0.0, 1.0709, 0.0712, 0.8267, 1.6318, 0.7217, 0.7295, 1.4462, 0.527]\n",
      "iteration: 234813\n",
      "w: [0.6671, 2.3561, 4.4066, 1.0581, 1.5232, 0.0, 1.044, 0.0697, 0.8137, 1.6663, 0.6943, 0.7393, 1.4921, 0.547]\n",
      "Loss in trainset: 0.3239\n",
      "Loss in testset: 0.2887\n",
      "iteration: 273786\n",
      "w: [0.6671, 2.3561, 4.364, 1.0358, 1.4846, 0.0138, 1.0873, 0.0167, 0.8664, 1.746, 0.6109, 0.8261, 1.5745, 0.5257]\n",
      "iteration: 312954\n",
      "w: [0.6671, 2.3561, 4.3598, 1.0022, 1.5032, 0.0124, 1.0612, 0.013, 0.8381, 1.69, 0.663, 0.7686, 1.5347, 0.5373]\n",
      "iteration: 352122\n",
      "w: [0.6671, 2.3561, 4.3617, 1.02, 1.5123, 0.0084, 1.0499, 0.0101, 0.8245, 1.6953, 0.6552, 0.7977, 1.5289, 0.5681]\n",
      "iteration: 391290\n",
      "w: [0.6671, 2.3561, 4.3621, 1.0194, 1.5164, 0.0, 1.0498, 0.0132, 0.8241, 1.7036, 0.6464, 0.8001, 1.5357, 0.5601]\n",
      "Loss in trainset: 0.3239\n",
      "Loss in testset: 0.2887\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 149817 TEST: 73978\n",
      "dataset built\n",
      "Loss in trainset: 0.3430\n",
      "Loss in testset: 0.3349\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10597e2c1e4444efbc9e00df7a66c343",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/19836 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.5491, 2.7136, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0, 0.7274]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b31baa0c501466e82b42896f3024cfc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/389943 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3336\n",
      "Loss in testset: 0.3280\n",
      "iteration: 38912\n",
      "w: [0.4079, 2.7898, 4.9993, 1.0134, 1.2202, 0.0148, 1.3781, 0.1135, 1.0192, 1.6887, 0.5917, 0.5655, 1.2016, 0.5861]\n",
      "iteration: 77824\n",
      "w: [0.4079, 2.7898, 4.7015, 1.0983, 1.1695, 0.0, 1.1602, 0.015, 0.8793, 1.6398, 0.6682, 0.8013, 1.2447, 0.5049]\n",
      "iteration: 116736\n",
      "w: [0.4079, 2.7898, 4.4048, 0.9036, 1.4284, 0.0, 1.0485, 0.01, 0.8198, 1.6169, 0.7227, 0.7204, 1.3494, 0.5222]\n",
      "Loss in trainset: 0.3137\n",
      "Loss in testset: 0.3083\n",
      "iteration: 155581\n",
      "w: [0.4079, 2.7898, 4.1262, 0.8416, 1.4877, 0.0064, 1.0626, 0.0746, 0.8792, 1.7883, 0.5575, 0.673, 1.782, 0.5479]\n",
      "iteration: 194493\n",
      "w: [0.4079, 2.7898, 4.1055, 0.8857, 1.6786, 0.0107, 0.9481, 0.0433, 0.7863, 1.7231, 0.6337, 0.8597, 1.6831, 0.5318]\n",
      "iteration: 233405\n",
      "w: [0.4079, 2.7898, 3.9927, 0.8958, 1.6051, 0.0, 1.0229, 0.0682, 0.8667, 1.7301, 0.6346, 0.7583, 1.6784, 0.4838]\n",
      "Loss in trainset: 0.3135\n",
      "Loss in testset: 0.3080\n",
      "iteration: 272250\n",
      "w: [0.4079, 2.7898, 3.9631, 0.9177, 1.6216, 0.0023, 0.9977, 0.0184, 0.8414, 1.7522, 0.6106, 0.7973, 1.6532, 0.5123]\n",
      "iteration: 311162\n",
      "w: [0.4079, 2.7898, 3.9384, 0.9576, 1.5687, 0.0, 0.9464, 0.0277, 0.7911, 1.7174, 0.649, 0.826, 1.615, 0.5513]\n",
      "iteration: 350074\n",
      "w: [0.4079, 2.7898, 3.9063, 0.9141, 1.5825, 0.0022, 0.9879, 0.0139, 0.8339, 1.7198, 0.6435, 0.7757, 1.6258, 0.5487]\n",
      "iteration: 388986\n",
      "w: [0.4079, 2.7898, 3.9015, 0.9086, 1.581, 0.0012, 0.9893, 0.0138, 0.8353, 1.7224, 0.6403, 0.7756, 1.6298, 0.5413]\n",
      "Loss in trainset: 0.3133\n",
      "Loss in testset: 0.3080\n"
     ]
    },
    {
     "data": {
      "image/png": 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qx/ILRQ4bNow777yTmJgYOnXqxJtvvklOTg7x8fEADBw4kPr16xceIUpISCAmJoamTZuSm5vLzJkzmTJlCu+++y4ANWvWpGbNmkVqeHt7Ex4eTvPmzQH35LCrr76a++67j/fee4/8/HwGDx7MLbfcctpLAoibl8POs9e2ok39YJ76di0L0g5y/cTF/GdgDM3CqlvdnohIlWDoFqnnrTx+h5YHpP79+3Pw4EFGjx5Neno60dHRzJ49u3Di9s6dO7Hb/zrQlZOTw6BBg9i9ezf+/v60aNGCzz77jP79+59T3c8//5zBgwfTvXt37HY7N910E2+//Xa57ltVdUO7+kTVCeSBKcls/+OY+z5u/2rLNW3qWt2aiIjH8vZ2f0v42LFj+Pv7W9yNZzt2zH2D35O/07KwGYqqZVLauwFXZYdy8hj8xWqWbvkDgEHdmjK8Z3Mc9vKbHC8iciHZt28fR44coU6dOlSrVq1cv2x0ITAMg2PHjnHgwAFCQkKoW/fUf7iX9vNbAamMFJDcCpwuXpq1kf8u3gbAFc1q8/Yt7QiupusliYicK8MwSE9P58iRI1a34tFCQkIIDw8/bcBUQKpgCkhF/ZCyh6e+XcuJfBeNalbjP3fE0Dxc85JERMrC6XRac3uNKsDb2xuHw1Hi8wpIFUwB6VTr9mTywJRk9hw5TjUfB6/9qy29NS9JREQqkdJ+fnvkhSKlcrq4fjA/DrmcLlE1OZbnZNDnq3l59kZdL0lERDyOApKUq9AAHybHd+L+rk0AeHfhFuI/WcmRY3kWdyYiIlJ6CkhS7rwcdp7u3ZK3bonGz9vOz5sO0nfiEjamV90b/IqISNWigCQV5vro+kx9qAsNaviz89Axbpy0lOlr91rdloiIyFkpIEmFalUviB8HX87lUbU4nu9k8BdreGmW5iWJiEjlpoAkFa5GgA+fxHfkgT/nJb23aAt3fbxC85JERKTSUkASU3g57Izs3ZK3b22Hn7edXzZn0HfiElL3aV6SiIhUPgpIYqq+besx9aEuRIS65yX9852l/Pir5iWJiEjlooAkpmtVL4hpD1/OPy5yz0sa8r81JMxM1bwkERGpNBSQxBLueUmdeOAK97yk93/eyl0fr+BwjuYliYiI9RSQxDIOu42R17Rk4m3t8Pd2uOclTVrMhr2alyQiItZSQBLLXXtJPaYOuoyGodXYdeg4/3x3CdM0L0lERCykgCSVQsu6QUwb3IV/XFSLE/kuHvnfGl6cmUqB02V1ayIicgFSQJJKI6Sae17SQ92aAvCfn7dy18crNS9JRERMp4AklYrDbuOpq1sw6bb2+Hs7WPx7BtdNXMz6vZlWtyYiIhcQBSSplPpcUpfvHr6MRjWrsfvwcW56dyk/pOyxui0REblAKCBJpdUi3H29pK7NanMi38WjX6bwwowNmpckIiIVTgFJKrXgat58fFdHBv05L+mDX7Zx58crOKR5SSIiUoEUkKTSc9htPHl1C94Z0J5qPg6W/P4H101YzLo9mpckIiIVQwFJPEbvNnX5blAXGtWsxp4jx+n3nuYliYhIxVBAEo/SPLw60x6+nG7N/5qXNH665iWJiEj5UkASjxNczZsP7+zIw1e65yV9uHgbAz9awR/ZuRZ3JiIiVYUCkngkh93GE71a8O6f85KWbvmDvhOXaF6SiIiUCwUk8WjXtKnL9w93IfLPeUk3vbuU79bstrotERHxcApI4vGahVXnh8GXc2Xz2uQWuHjsq18Z96PmJYmISNkpIEmVEOzvnpc05KooAD5aso3bP1yueUkiIlImCkhSZdjtNob3bM57t3cgwMfBsq2HNC9JRETKRAFJqpyrLw7n+4e70LhWQOG8pKmrNS9JRERKTwFJqqSLwqrz/cNduKpFHXILXAz7+lee+3E9+ZqXJCIipaCAJFVWsL83/x0YwyN/zkv6eMl2bv/vcjI0L0lERM5CAUmqNLvdxrCezXn/Dve8pOXbDtF3wmLW7j5idWsiIlKJKSDJBaFX63B+GNyFJrUC2Jt5gn7vJfFtsuYliYjI6SkgyQUjqk51vh/che4t6pBX4GL4//3K2GmalyQiIqdSQJILSpCfNx8MjOHR7hcB8MnS7QzQvCQRESmmUgSkSZMmERkZiZ+fH7GxsaxYsaLEsVOnTiUmJoaQkBACAgKIjo5mypQpRcaMHTuWFi1aEBAQQI0aNYiLi2P58uVFxkRGRmKz2Yo8XnrppQrZP6lc7HYbj/Voxn/u6ECgrxcrth3iOs1LEhGRv7E8IH311VcMGzaMMWPGsHr1atq2bUuvXr04cODAaceHhoYyatQokpKSWLt2LfHx8cTHxzNnzpzCMc2aNWPixIn89ttvLF68mMjISHr27MnBgweLbGvcuHHs27ev8DFkyJAK3VepXHq2dl8vqUntAPb9OS/p/1btsrotERGpBGyGYRhWNhAbG0vHjh2ZOHEiAC6Xi4iICIYMGcKIESNKtY327dvTp08fxo8ff9rns7KyCA4OZt68eXTv3h1wH0EaOnQoQ4cOLVPfJ7eZmZlJUFBQmbYhlUPWiXyGffUr81L3A3Bn50Y8c20rvB2W//tBRETKWWk/vy39BMjLyyM5OZm4uLjCdXa7nbi4OJKSks76esMwSExMJC0tja5du5ZY4z//+Q/BwcG0bdu2yHMvvfQSNWvWpF27drz66qsUFBSUWCs3N5esrKwijwrhcrofYpogP2/+c0cHhsa55yVNTtrBgA+Wc/Co5iWJiFyoLA1IGRkZOJ1OwsLCiqwPCwsjPT29xNdlZmYSGBiIj48Pffr0YcKECfTo0aPImOnTpxMYGIifnx9vvPEGc+fOpVatWoXPP/LII3z55ZcsWLCABx54gBdffJEnn3yyxJoJCQkEBwcXPiIiIsq412ex8r/wn26w4+wBUcqP3W5jaFwzPhgYQ3VfL1Zsd89LStl1xOrWRETEApaeYtu7dy/169dn6dKldO7cuXD9k08+yaJFi06ZWH2Sy+Vi69atZGdnk5iYyPjx4/n+++/p1q1b4ZicnBz27dtHRkYGH3zwAfPnz2f58uXUqVPntNv86KOPeOCBB8jOzsbX1/eU53Nzc8nN/euIQlZWFhEREeV7is1ZABPaw5Ed7p8v7gc9noPgBuWzfSmVLQezuf/TVWw5mIOPl53nb7iYm2MqKBCLiIipPOIUW61atXA4HOzfv7/I+v379xMeHl7i6+x2O1FRUURHRzN8+HD69etHQkJCkTEBAQFERUVx6aWX8uGHH+Ll5cWHH35Y4jZjY2MpKChg+/btp33e19eXoKCgIo9y5/CC++ZD+zsBG6z7BiZ2hEWvQP7x8q8np9W0diDfP9yFHq3CyCtw8eQ3axn9wzryCnS9JBGRC4WlAcnHx4cOHTqQmJhYuM7lcpGYmFjkiNLZuFyuIkd3yjImJSUFu91e4hEm0wTUgr5vw/0LoWFnyD8GC16ASZ1gww9g7Zz6C0Z1P2/ev70Dj8U1A+DTpB0M+O8yzUsSEblAeFndwLBhw7jzzjuJiYmhU6dOvPnmm+Tk5BAfHw/AwIEDqV+/fuERooSEBGJiYmjatCm5ubnMnDmTKVOm8O677wLuU2svvPACffv2pW7dumRkZDBp0iT27NnDv/71LwCSkpJYvnw5V155JdWrVycpKYnHHnuM22+/nRo1aljziyiuXjTEz4J138Lc0XBkJ3w9ECL/Ade8DGGtre6wyrPbbTwadxEX1w9i6JcprNx+mOsmLObd29vTrmEl+e9EREQqhOUBqX///hw8eJDRo0eTnp5OdHQ0s2fPLpy4vXPnTuz2vw505eTkMGjQIHbv3o2/vz8tWrTgs88+o3///gA4HA42btzI5MmTycjIoGbNmnTs2JFffvmF1q3docLX15cvv/ySsWPHkpubS+PGjXnssccYNmyY+b+AM7HZoE0/aH4NLH4Tlr4N23+B9y6HmHvgyqehWqjVXVZ53VuG8f3gLoXzkvq/v8w9L6mj5iWJiFRVll8HyVNZch2kwzvgp2cgdZr7Z/8acOUo6BDvnr8kFeroiXyGf/0rP21wz5m7/dKGjL62NT5eul6SiIinKO3ntwJSGVl6ochtP8OsEXBgvfvnOq3hmpeg8emvBSXlx+UymLTgd/49bxOGATGNavDO7e2pU93P6tZERKQUFJAqmOVX0nYWQPLH7gncxw+717XsCz2fhxqNzO/nAjN/434e/V8KR3MLCAvy5b3bO2hekoiIB/CIr/nLeXB4Qaf7YMhq6Hgf2OzuU28TO8L85yEvx+oOq7SrWoTxw+AuXFQnkP1ZufR/fxlfrdxpdVsiIlJOdASpjCw/glTc/vUw6yn3JG6AoPrQYxxcfJN7srdUiOzcAoZ/ncKc9e55SQNiGzLmOs1LEhGprHSKrYJVuoAE7mskpf4IP41yXxYA3NdSuvol92UDpEK4XAbvLtrCaz+lYRjQoVEN3h3QnjpBmpckIlLZKCBVsEoZkE7KPw5LJ8Lif7svNIkN2t8BV42GwNpWd1dlLdh4gEe+XMPREwXUC/Zj1tCuBPt7W92WiIj8jeYgXci8/eGKJ2DwKvf93DBg9acwoQMkTQJnvtUdVklXtqjDtMGX0zC0GnszT/DG3E1WtyQiImWkgFSVBdeHfh9C/Gyo2xZyM2HO0/DuZfD7PKu7q5Ia1wrghRsvBmDKsh2kpR+1uCMRESkLBaQLQaPOcN8CuO5tqFYLMjbBZzfBF7fAH1us7q7K+cdFtenVOgyny2DMtHXoLLaIiOdRQLpQ2B3Q4U4YkgyXPgx2L9g0CybFuu/1lqsjHeXpmT6t8PWys2zrIWb+lm51OyIico4UkC40/iFw9Yvw0FJo2h1c+bDkLff8pJQvwOWyusMqISK0Gg9e0RSAF2Zs4FhegcUdiYjIuVBAulDVbg63fwu3fgWhTSB7P3z/EHzYA3avsrq7KuGhbk2pH+LP3swTvLtQpzJFRDyJAtKFzGaD5lfDoGUQ9xz4BMKeVfDf7vDdQ3BUp4bOh5+3g2evbQnA+z9vZecfxyzuSERESksBScDLFy4f6p6f1PY297pfv3Cfdlv8JhTkWtmdR+vVOpzLo2qRV+Bi3PQNVrcjIiKlpIAkf6keDje+C/cmQv0OkJcN88bAO5dC2iz3lbrlnNhsNsb2bYWX3ca81P0sTDtgdUsiIlIKCkhyqgYxcM88uOE9CAyDQ1vhf7e4Lw1wUBc/PFdRdapz52WRAIz7cQN5BZoILyJS2SkgyenZ7RB9q/u0W5eh4PCBLYnwbmeY/TQcP2J1hx7l0biLqBXoy9aMHD5ass3qdkRE5CwUkOTMfKtDj+fcE7mb9wZXASyb5J6flDwZXE6rO/QIQX7ePHV1cwAmJG5mf9YJizsSEZEzUUCS0qnZFG79n/vSALWawbEM+PER+OBK2JFkdXce4ab2DWjXMIScPCcvzdpodTsiInIGCkhybqLi3BeZ7JUAvsGw71f4+Gr45h7I3GN1d5Wa3W7jub6tsdnguzV7WLX9kNUtiYhICRSQ5Nw5vKHzIPf8pPZ3AjZY9w1MjIFFr0L+cas7rLQuaRBC/5gIAEb/sB6nS98MFBGpjBSQpOwCa0Pft+H+hdCwM+QfgwXPw6ROsOEHXRagBE/0ak6Qnxcb9mXxvxU7rW5HREROQwFJzl+9aIifBTd9CEH14chO+HogTL4O9q+3urtKp2agL8N6NAPgtZ/SOJyTZ3FHIiJSnAKSlA+bDdr0g8EroeuT4PCF7b/Ae5fDjMfhmObb/N3tlzaieVh1jhzL5/W5aVa3IyIixSggSfnyCYCrRrmDUsu+YLhg5QcwoT2s+ACcuqs9gJfDzti+rQH4YvlO1u/NtLgjERH5OwUkqRg1GkH/KXDnj1CnFRw/DDMfh/e7wrafre6uUujctCZ9LqmLy4Cx09ZjaM6WiEiloYAkFatxV3jgF+j9GvjXgAPr3XOTvroDDu+wujvLjerdEn9vByu3H2bar3utbkdERP6kgCQVz+EFne6DIauh431gs0PqNPe33ea/AHk5VndomXoh/jx8ZVMAXpyZSk6uTkGKiFQGCkhinmqh0Oc1eHAxRP4DCk7Az6/AxI7w2zcX7GUB7v1HExqGVmN/Vi4T5v9udTsiIoICklghrLV7btLNn0JwQ8jaA9/eAx9f474y9wXGz9vB6GtbAfDh4q1sPZhtcUciIqKAJNaw2aDV9TB4BVz5DHhXg51J8P4VMO0RyMmwukNTdW9Zh27Na5PvNBg3fYMmbIuIWEwBSazl7Q9XPAGDV8HF/QADVk+Gt9tD0jvgzLe6Q1PYbDZGX9sKb4eNhWkHSUw9YHVLIiIXNAUkqRyC60O/DyF+NoRfArmZMGckvHsZ/D7P6u5M0aR2IHdf3hiAcdM3cCLfaXFHIiIXLgUkqVwadXbf2+26t6BaTcjYBJ/dBF/cAn9ssbq7CjfkqouoU92XnYeO8eHibVa3IyJywVJAksrH7oAOd7kvC3Dpw2D3gk2z4J1LYe4YyD1qdYcVJtDXi6d7twRg4vzf2XvkuMUdiYhcmBSQpPLyD4GrX4SHlkLT7uDMgyVvwoQOkPIFuFxWd1ghro+uR8fIGhzPd/LizFSr2xERuSApIEnlV7s53P4t3Pol1GgM2fvh+4fgwx6wO9nq7sqdzWZjbN/W2G0wfe0+krb8YXVLIiIXnEoRkCZNmkRkZCR+fn7ExsayYsWKEsdOnTqVmJgYQkJCCAgIIDo6milTphQZM3bsWFq0aEFAQAA1atQgLi6O5cuXFxlz6NAhBgwYQFBQECEhIdxzzz1kZ+v6M5WWzQbNr4GHl0Pcc+ATCHtWwX+vgu8egqPpVndYrlrXC+a22IYAPPfjegqcVfNomYhIZWV5QPrqq68YNmwYY8aMYfXq1bRt25ZevXpx4MDpv+YcGhrKqFGjSEpKYu3atcTHxxMfH8+cOXMKxzRr1oyJEyfy22+/sXjxYiIjI+nZsycHDx4sHDNgwADWr1/P3LlzmT59Oj///DP3339/he+vnCcvX7h8KAxJhra3udf9+oX7tNviN6Eg18ruytXwHs0JqebNxvSjfLZM960TETGTzbD4inSxsbF07NiRiRMnAuByuYiIiGDIkCGMGDGiVNto3749ffr0Yfz48ad9Pisri+DgYObNm0f37t1JTU2lVatWrFy5kpiYGABmz55N79692b17N/Xq1TtrzZPbzMzMJCgoqJR7K+Vu9yqY9STs+fNUW2gT6PUiNLvafdTJw01ZtoNnv19HkJ8XCx7vRs1AX6tbEhHxaKX9/Lb0CFJeXh7JycnExcUVrrPb7cTFxZGUlHTW1xuGQWJiImlpaXTt2rXEGv/5z38IDg6mbdu2ACQlJRESElIYjgDi4uKw2+2nnIo7KTc3l6ysrCIPqQQaxMA98+CG9yAwDA5thf/d4r40wMFNVnd33m7r1JBWdYPIOlHAaz+lWd2OiMgFw9KAlJGRgdPpJCwsrMj6sLAw0tNLnlOSmZlJYGAgPj4+9OnThwkTJtCjR48iY6ZPn05gYCB+fn688cYbzJ07l1q1agGQnp5OnTp1ioz38vIiNDS0xLoJCQkEBwcXPiIiIsqyy1IR7HaIvtV92q3LUHD4wJZEeLczzH4ajh+xusMyc9htPHd9awC+XLmLtbuPWNuQiMgFwvI5SGVRvXp1UlJSWLlyJS+88ALDhg1j4cKFRcZceeWVpKSksHTpUq6++mpuvvnmEuc1lcbIkSPJzMwsfOzates890LKnW916PEcDFoGza4BVwEsm+Sen5Q8GVyeeWXqjpGh3BBdD8OAMdPW43LpPm0iIhXN0oBUq1YtHA4H+/fvL7J+//79hIeHl/g6u91OVFQU0dHRDB8+nH79+pGQkFBkTEBAAFFRUVx66aV8+OGHeHl58eGHHwIQHh5+SlgqKCjg0KFDJdb19fUlKCioyEMqqZpN4bYv3ZcGqNUMjmXAj4/AB1fCzmVWd1cmI3u3JMDHwZqdR5i6Zo/V7YiIVHmWBiQfHx86dOhAYmJi4TqXy0ViYiKdO3cu9XZcLhe5uWf+9tLfx3Tu3JkjR46QnPzXNXTmz5+Py+UiNjb2HPdCKq2oOPdFJnslgG8w7PsVPuoF39wDmZ4VMsKC/BjS/SIAXpq1kawTF8ZNfEVErGL5KbZhw4bxwQcfMHnyZFJTU3nooYfIyckhPj4egIEDBzJy5MjC8QkJCcydO5etW7eSmprK66+/zpQpU7j99tsByMnJ4emnn2bZsmXs2LGD5ORk7r77bvbs2cO//vUvAFq2bMnVV1/Nfffdx4oVK1iyZAmDBw/mlltuKdU32MSDOLyh8yD3/KT2dwI2WPcNTIyBRa9CvufcyuPuLo1pUiuAjOxc3p632ep2RESqNC+rG+jfvz8HDx5k9OjRpKenEx0dzezZswsnbu/cuRO7/a8cl5OTw6BBg9i9ezf+/v60aNGCzz77jP79+wPgcDjYuHEjkydPJiMjg5o1a9KxY0d++eUXWrduXbidzz//nMGDB9O9e3fsdjs33XQTb7/9trk7L+YJrA1934aYu2HWU7BrGSx4HtZ8Cj1fgJbXVfrLAvh42Rl9XSvu+nglnyzdzi2dIoiqU93qtkREqiTLr4PkqXQdJA9mGLDuW5g7GrL+PNXWuCtc/RKEtT7zayuBeyevZF7qAS6PqsWUezphq+TBTkSkMvGI6yCJWMJmgzb9YPBK6PokOHxh28/w3uUw43E4dsjqDs/o2Wtb4eNlZ/HvGcxZv//sLxARkXOmgCQXLp8AuGqUOyi17AuGC1Z+ABPaw4oPwFlgdYen1ahmAPf/owkAz8/YwIl8z7x8gYhIZaaAJFKjEfSfAgOnQZ1WcPwwzHwc3u8K236xurvTGnRlU+oF+7H78HHeW7TF6nZERKocBSSRk5pcAQ/8Ar1fA/8acGA9TL4Wvh4IhyvXzWKr+XjxdJ+WALy7cAu7Dh2zuCMRkapFAUnk7xxe0Ok+GLIaOt4HNjts+AEmdYL5L0BejtUdFurTpi6dm9Qkt8DFCzNSrW5HRKRKUUASOZ1qodDnNXhwMUT+AwpOwM+vwMSO8Ns37m/CWcxmszG2b2scdhuz16ezeHOG1S2JiFQZCkgiZxLWGu78EW7+FIIbui8L8O098PE17itzW6x5eHXuuLQRAGN/XE++02VxRyIiVYMCksjZ2GzQ6noYvAKufAa8q8HOJHj/Cpj2CORYe+TmsbhmhAb48PuBbCYv3W5pLyIiVYUCkkhpefvDFU+4LwtwcT/AgNWT4e32kPQOOK25P1pwNW+e7NUcgDfnbebA0ROW9CEiUpUoIImcq+AG0O9DiJ8N4ZdAbibMGQnvdoHfE8/++gpwc0wElzQIJju3gFdmp1nSg4hIVaKAJFJWjTrD/QvhuregWk3ISIPP/gn/uxX+MPfaRHa7jef6um+T8k3yblbvPGxqfRGRqkYBSeR82B3Q4S73ZQEuHQR2L0ibCf+5EjJ3m9pKu4Y16NehAQBjp63H5bL+m3YiIp5KAUmkPPiHwNUJ8NBSCGvjPu320zOmt/HU1S2o7uvF2t2ZfL1ql+n1RUSqCgUkkfJUuznc8I77ApPrv4Oti8wtX92XR+MuAuCVOWlkHrNm4riIiKdTQBIpb3UvgZh73MuznjT92213XhZJVJ1ADuXk8ca8TabWFhGpKhSQRCrCVaOgWi04uBGWv2dqaW+HnbHXuSdsT1m2g43pWabWFxGpChSQRCqCfw2IG+teXvgSHE03tfzlF9Xi6tbhOF0GY6etx6gEt0YREfEkCkgiFSV6ANSPgbxs+OlZ08uP6tMSXy87y7YeYsZv+0yvLyLiyRSQRCqK3Q69XwVs8NvXsH2JqeUjQqvxULemALwwI5VjeQWm1hcR8WQKSCIVqX576HCne3nmE+A0N6Q8eEVTGtTwZ1/mCd5ZYO7FK0VEPJkCkkhF6z7GPSfpwHpY+V9TS/t5O3imTysA/vPzVnb8kWNqfRERT6WAJFLRqoVC99Hu5QUvQPYBU8v3ah3GPy6qRZ7TxfjpG0ytLSLiqRSQRMzQ/k6o2xZys2DeWFNL22w2xlzXCi+7jXmpB1iQZm5AExHxRApIImawO6D36+7llM9h1wpTy0fVqc5dl0UCMO7HDeQVuEytLyLiaRSQRMwS0RGib3cvz3wcXE5Tyz8adxG1An3ZlpHDR0u2mVpbRMTTKCCJmCluLPgFw75fIfljU0tX9/NmxDUtAJiQuJn9WSdMrS8i4kkUkETMFFgbrnzGvZw4HnL+MLX8P9vVp13DEHLynCTMTDW1toiIJylTQJo8eTIzZswo/PnJJ58kJCSEyy67jB07dpRbcyJVUszdEHYxnDgCic+ZWtputzGu78XYbPB9yl5Wbj9kan0REU9RpoD04osv4u/vD0BSUhKTJk3ilVdeoVatWjz22GPl2qBIlePwgt6vuZdXfwp7kk0t36ZBMLd0jABgzA/rcbp0nzYRkeLKFJB27dpFVFQUAN9//z033XQT999/PwkJCfzyyy/l2qBIldSoM1zSHzDcV9h2mfutssd7NifIz4sN+7L4YsVOU2uLiHiCMgWkwMBA/vjDPXfip59+okePHgD4+flx/Pjx8utOpCrrMQ58qruPIKV8ZmrpmoG+DO/ZHIDXf0rjcE6eqfVFRCq7MgWkHj16cO+993LvvfeyadMmevfuDcD69euJjIwsz/5Eqq7q4XDlSPfyvLFwzNz5QANiG9IivDpHjuXz+tw0U2uLiFR2ZQpIkyZNonPnzhw8eJBvv/2WmjVrApCcnMytt95arg2KVGmd7ofaLeHYH+7bkJjIy2FnbN/WAHyxfCfr92aaWl9EpDKzGYahGZplkJWVRXBwMJmZmQQFBVndjniybT/D5OvAZof7F7pvSWKiwV+sZvrafXSMrMHXD3TGZrOZWl9ExEyl/fwu0xGk2bNns3jx4sKfJ02aRHR0NLfddhuHDx8uyyZFLlyNu0Lrf4LhsmTC9qg+LfH3drBy+2F+SNlram0RkcqqTAHpiSeeICsrC4DffvuN4cOH07t3b7Zt28awYcPKtUGRC0LP58E7AHYth7VfmVq6brA/g69yfyv1xZmpZOcWmFpfRKQyKlNA2rZtG61atQLg22+/5dprr+XFF19k0qRJzJo1q1wbFLkgBNeHK550L88dDSfMnQ907z8a06hmNQ4czWXC/M2m1hYRqYzKFJB8fHw4duwYAPPmzaNnz54AhIaGFh5ZOheTJk0iMjISPz8/YmNjWbGi5DudT506lZiYGEJCQggICCA6OpopU6YUPp+fn89TTz1FmzZtCAgIoF69egwcOJC9e4ueOoiMjMRmsxV5vPTSS+fcu0i5uXQQ1LwIcg7AggRTS/t6ORh9rfsfPR8t3saWg9mm1hcRqWzKFJAuv/xyhg0bxvjx41mxYgV9+vQBYNOmTTRo0OCctvXVV18xbNgwxowZw+rVq2nbti29evXiwIEDpx0fGhrKqFGjSEpKYu3atcTHxxMfH8+cOXMAOHbsGKtXr+bZZ59l9erVTJ06lbS0NPr27XvKtsaNG8e+ffsKH0OGDDnH34RIOfLygWtedi+v+A/sX29q+e4tw7iyeW3ynQbjftyAvr8hIheyMn2LbefOnQwaNIhdu3bxyCOPcM899wDw2GOP4XQ6efvtt0u9rdjYWDp27MjEiRMBcLlcREREMGTIEEaMGFGqbbRv354+ffowfvz40z6/cuVKOnXqxI4dO2jYsCHgPoI0dOhQhg4dWupe/07fYpMK89XtkPojNOoCd80AE79VtvVgNr3e/Jl8p8F/B8YQ1yrMtNoiImao0G+xNWzYkOnTp/Prr78WhiOAN95445zCUV5eHsnJycTFxf3VkN1OXFwcSUlJZ329YRgkJiaSlpZG165dSxyXmZmJzWYjJCSkyPqXXnqJmjVr0q5dO1599VUKCkqenJqbm0tWVlaRh0iF6JUAXv6wYwms+9bU0k1qB3LP5U0AGDd9AyfynabWFxGpLLzK+kKn08n3339PamoqAK1bt6Zv3744HI5SbyMjIwOn00lYWNF/pYaFhbFx48YSX5eZmUn9+vXJzc3F4XDwzjvvFN7upLgTJ07w1FNPceuttxZJio888gjt27cnNDSUpUuXMnLkSPbt28e///3v024nISGB554z987rcoEKiYCuw2H+8/DTM9CsF/hWN638kKui+G7NbnYeOsZ/f9nK4KsuMq22iEhlUaZTbL///ju9e/dmz549NG/uvp9TWloaERERzJgxg6ZNm5ZqO3v37qV+/fosXbqUzp07F65/8sknWbRoEcuXLz/t61wuF1u3biU7O5vExETGjx/P999/T7du3YqMy8/P56abbmL37t0sXLjwjIfSPvroIx544AGys7Px9fU95fnc3Fxyc3MLf87KyiIiIkKn2KRi5J+Ady6Fw9vgsiHuywCY6IeUPTz6ZQr+3g4Sh19BvRB/U+uLiFSUCj3F9sgjj9C0aVN27drF6tWrWb16NTt37qRx48Y88sgjpd5OrVq1cDgc7N+/v8j6/fv3Ex4eXnLTdjtRUVFER0czfPhw+vXrR0JC0W/95Ofnc/PNN7Njxw7mzp171hATGxtLQUEB27dvP+3zvr6+BAUFFXmIVBhvP7jmFffysnfhoLn3Suvbth6dIkM5nu/khZmpptYWEakMyhSQFi1axCuvvEJoaGjhupo1a/LSSy+xaNGiUm/Hx8eHDh06kJiYWLjO5XKRmJhY5IjS2bhcriJHd06Go82bNzNv3rzCe8WdSUpKCna7nTp16pS6rkiFatYTml0DrgL3FbZN/FaZzWZjbN/W2G0wY+0+lm7JMK22iEhlUKaA5Ovry9GjR09Zn52djY+Pzzlta9iwYXzwwQdMnjyZ1NRUHnroIXJycoiPjwdg4MCBjBw5snB8QkICc+fOZevWraSmpvL6668zZcoUbr/9dsAdjvr168eqVav4/PPPcTqdpKenk56eTl5eHgBJSUm8+eab/Prrr2zdupXPP/+cxx57jNtvv50aNWqU5VciUjGuTgCHL2xbBBt+MLV0q3pBDIhtBMBz0zZQ4DT3FigiIlYq0yTta6+9lvvvv58PP/yQTp06AbB8+XIefPDB015v6Ez69+/PwYMHGT16NOnp6URHRzN79uzCids7d+7Ebv8rx+Xk5DBo0CB2796Nv78/LVq04LPPPqN///4A7Nmzh2nTpgEQHR1dpNaCBQvo1q0bvr6+fPnll4wdO5bc3FwaN27MY489ptukSOUT2hgufwwWvQRzRsFFPcAnwLTyw3s248e1e0nbf5Qpy3YQ36WxabVFRKxUpknaR44c4c477+THH3/E29sbcB+5uf766/n4449P+Tp9VaTrIIlp8o/DpE5wZCdcPgzixpha/rNlO3jm+3VU9/Ni4ePdqBl46pcYREQ8RWk/v8sUkE76/fffC7/m37JlS6Kiosq6KY+jgCSm2jgDvrwN7N4waBnUMu9vzeky6DtxMev3ZnFLxwheuukS02qLiJS3cg9I53L6qaRrCVUlCkhiKsOAz/vB7/MgKg4GfGPqFbZXbT9Ev/eSsNngh4e7cEmDENNqi4iUp9J+fpd6DtKaNWtKNc5m4v9pi1wwbDb31/7fudQdktJmQos+ppWPiQzlxnb1+W7NHkb/sJ6pD12G3a6/dRGpus7rFNuFTEeQxBKJ4+CX1yGkITy8ArzNu4DjgawTXPnaQnLynLza7xL+FRNhWm0RkfJSoReKFBGL/GM4BDVwT9he/IappesE+fFId/dtR16evZGsE/mm1hcRMZMCkogn8QmAXi+4lxe/CYe2mVo+vktjmtQOICM7j7fmbTa1toiImRSQRDxNq+uh8RXgzIXZI88+vhz5eNkZfW0rACYv3c7m/adeMFZEpCpQQBLxNDYb9H4V7F6waRZsmmNq+W7N6xDXMowCl8FzP25A0xhFpCpSQBLxRLWbw6WD3MuznoL8E6aWH31tK3y87Cz+PYM569NNrS0iYgYFJBFPdcWTUL0uHN4GSyeYWrphzWo80LUJAOOnp3I8z2lqfRGRiqaAJOKpfKtDz+fdy7+87v5mm4kGdYuiXrAfe44c571FW0ytLSJS0RSQRDzZxTdBo8uh4DjMedrU0v4+Dkb1cU/Yfm/RFnYdOmZqfRGRiqSAJOLJTk7Ytjkg9Uf4PdHU8r3bhNO5SU1yC1w8P2ODqbVFRCqSApKIpwtrBbEPuJdnPQUFeaaVttlsPHd9axx2G3PW7+eXzQdNqy0iUpEUkESqgm4jIKAO/LEZlk0ytXSzsOoM7NwIgLHT1pPvdJlaX0SkIiggiVQFfsHQY5x7edGrkLnH1PJD45pRM8CHLQdzmLx0u6m1RUQqggKSSFVxSX+IiIX8HPjpGVNLB/t78+TVzQF4c95mDhw197pMIiLlTQFJpKqw26H3a2Czw/qpsO1nU8v/q0MEbRsEk51bwMuz0kytLSJS3hSQRKqSupdAzD3u5ZlPgDPftNJ2u42xfVsD8O3q3STvOGxabRGR8qaAJFLVXDUKqtWEgxth+fumlm7XsAb/6tAAcE/Ydrp0nzYR8UwKSCJVjX8NiBvrXl74Ehw1915pT17dguq+Xvy2J5OvV+0ytbaISHlRQBKpiqJvh/odIO8ozB1tauna1X0Z2qMZAK/OSSPzmHmn+UREyosCkkhVdHLCNjZY+xXsWGpq+YGdG3FRnUAO5eTxxrxNptYWESkPCkgiVVX99tDhTvfyzCfAWWBaaW+HvXDC9pRlO9iYnmVabRGR8qCAJFKVXTXaPSdp/zpY9ZGppbtE1eKai8NxugzG/LAew9CEbRHxHApIIlVZQE246ln38vznIdvce6WN6tMSP287y7cdYvrafabWFhE5HwpIIlVdh7ugblvIzYR5Y00t3aBGNR66IgqAF2emcizPvNN8IiLnQwFJpKqzO/6csA2kfAa7Vppa/oErmtCghj/7Mk8wacHvptYWESkrBSSRC0FEJ/dX/wFmDgeX07TSft4Onr22FQAf/LyN7Rk5ptUWESkrBSSRC0XcWPANhn2/wurJppbu2SqMf1xUizyni/HTN5haW0SkLBSQRC4UgbXdtyEBSBwHxw6ZVtpmszHmutZ42W0kbjzAgo0HTKstIlIWCkgiF5KYeyDsYjh+GBKfM7V0VJ1A4rtEAjBu+gZyC8w7zScicq4UkEQuJA4v6P2qezl5MuxZbWr5R7pfRO3qvmzLyOGjxdtNrS0ici4UkEQuNI0ug0v6A4b7Ctsul2mlq/t5M+LqFgBMmL+Z9MwTptUWETkXCkgiF6Ie48CnOuxZBSmfm1r6xnb1ad8whGN5ThJmpZpaW0SktBSQRC5E1cOh2wj38rwx7jlJJrHbbYy7/mJsNvghZS8rtpk3WVxEpLQUkEQuVLEPQO0WcOwPmP+CqaUvrh/MLR0bAjBm2nqcLt2nTUQql0oRkCZNmkRkZCR+fn7ExsayYsWKEsdOnTqVmJgYQkJCCAgIIDo6milTphQ+n5+fz1NPPUWbNm0ICAigXr16DBw4kL179xbZzqFDhxgwYABBQUGEhIRwzz33kJ2dXWH7KFLpOLzhmlfcy6s+hH1rTS3/RK/mBPt7k7oviy9W7DS1tojI2VgekL766iuGDRvGmDFjWL16NW3btqVXr14cOHD666SEhoYyatQokpKSWLt2LfHx8cTHxzNnzhwAjh07xurVq3n22WdZvXo1U6dOJS0tjb59+xbZzoABA1i/fj1z585l+vTp/Pzzz9x///0Vvr8ilUqTK6D1jWC43BO2DfOO5IQG+DC8ZzMAXv8pjcM5eabVFhE5G5thmPj/iKcRGxtLx44dmThxIgAul4uIiAiGDBnCiBEjSrWN9u3b06dPH8aPH3/a51euXEmnTp3YsWMHDRs2JDU1lVatWrFy5UpiYmIAmD17Nr1792b37t3Uq1fvrDWzsrIIDg4mMzOToKCgUu6tSCWUuQcmdoT8HLjxfWh7i2mlC5wurp2wmI3pRxkQ25AXbmxjWm0RuTCV9vPb0iNIeXl5JCcnExcXV7jObrcTFxdHUlLSWV9vGAaJiYmkpaXRtWvXEsdlZmZis9kICQkBICkpiZCQkMJwBBAXF4fdbmf58uWn3UZubi5ZWVlFHiJVQnB9uOIJ9/JPz8KJTNNKeznsjO3bGoAvVuxk3R7zaouInImlASkjIwOn00lYWFiR9WFhYaSnp5f4uszMTAIDA/Hx8aFPnz5MmDCBHj16nHbsiRMneOqpp7j11lsLk2J6ejp16tQpMs7Ly4vQ0NAS6yYkJBAcHFz4iIiIOJddFancLn0YakZBzgFY+JK5pZvU5Lq29TAMGDttPRYf1BYRASrBHKSyqF69OikpKaxcuZIXXniBYcOGsXDhwlPG5efnc/PNN2MYBu++++551Rw5ciSZmZmFj127dp3X9kQqFS+fvyZsL38f9pt7Q9mne7fA39vBqh2H+T5lj6m1RUROx9KAVKtWLRwOB/v37y+yfv/+/YSHh5f4OrvdTlRUFNHR0QwfPpx+/fqRkJBQZMzJcLRjxw7mzp1b5DxjeHj4KZPACwoKOHToUIl1fX19CQoKKvIQqVKiukOLa8Fwmj5hu26wP4OvigIgYeZGsnMLTKstInI6lgYkHx8fOnToQGJiYuE6l8tFYmIinTt3LvV2XC4Xubm5hT+fDEebN29m3rx51KxZs8j4zp07c+TIEZKTkwvXzZ8/H5fLRWxs7HnskYiHuzoBvPxhx2JY962ppe/9R2Mia1bjwNFcJiRuNrW2iEhxlp9iGzZsGB988AGTJ08mNTWVhx56iJycHOLj4wEYOHAgI0eOLByfkJDA3Llz2bp1K6mpqbz++utMmTKF22+/HXCHo379+rFq1So+//xznE4n6enppKenk5fn/hpxy5Ytufrqq7nvvvtYsWIFS5YsYfDgwdxyyy2l+gabSJUV0hD+Mdy9/NMzkHvUtNK+Xg5GX9cKgI+WbGPLQV2XTESs42V1A/379+fgwYOMHj2a9PR0oqOjmT17duHE7Z07d2K3/5XjcnJyGDRoELt378bf358WLVrw2Wef0b9/fwD27NnDtGnTAIiOji5Sa8GCBXTr1g2Azz//nMGDB9O9e3fsdjs33XQTb7/9dsXvsEhld9kQ9/3ZDm+DRa9Az9NfPqMiXNUijKta1GH+xgOM+3EDn8R3xGazmVZfROQky6+D5Kl0HSSp0jbNgS9uBrsXPJQEtZuZVnpbRg693viZPKeLDwbG0KNV2NlfJCJSSh5xHSQRqaSa9YJmV4OrAGaZO2G7ca0A7vlHYwDGT9/AiXynabVFRE5SQBKR07v6JXD4wtaFkDrN1NKDr4wiPMiPnYeO8cHPW02tLSICCkgiUpLQxnD5UPfy7KchL8e00gG+Xozs3QKASQt/Z8+R46bVFhEBBSQROZMuQyG4IWTthl9eN7V037b16NQ4lBP5Ll6ckWpqbRERBSQRKZlPNfe1kQCWToA/tphW2mazMfa61thtMOO3fSz9PcO02iIiCkgicmYt+kDT7uDMg1lPmTphu1W9IG6/tBEAY39cT4HTZVptEbmwKSCJyJnZbO77tNm94fe5kDbL1PLDejSjRjVvNu3PZsqyHabWFpELlwKSiJxdrSj3BSQBZj8F+eZNmg6p5sPjvZoD8O+5m8jIzj3LK0REzp8CkoiUTtfHIag+HNkJi980tfQtHRtycf0gjp4o4NXZaabWFpELkwKSiJSOTwD0esG9vPgNOLTNtNIOu43n+rYG4OvkXfy664hptUXkwqSAJCKl1+oGaNwVnLkw52lTS3doFMo/29XHMGD0tPW4XLpLkohUHAUkESk9mw16v+a+R1vaTNj0k6nlR1zTgkBfL37ddYRvVu82tbaIXFgUkETk3NRuDpc+5F6e/RQUmDdpuk6QH490jwLgldkbyTqRb1ptEbmwKCCJyLm74ikIDIdDW90XkDTRXZc1pkntADKy83hr3mZTa4vIhUMBSUTOnW916Pm8e/nn1+DILtNK+3jZGXude8L25KXb2bz/qGm1ReTCoYAkImXTph806gIFx02fsN21WW16tAqjwGUw9sf1GCZe3VtELgwKSCJSNjYb9H4VbA5InQZb5pta/tk+rfDxsrPk9z+YvS7d1NoiUvUpIIlI2YW1hk73u5dnPgkFeaaVblizGg92bQLA8zNSOZ7nNK22iFR9Ckgicn66jYCA2vDHZlj+rqmlH+oWRf0Qf/YcOc67i7aYWltEqjYFJBE5P/4h0GOce3nhy5C117zSPg5G9WkJwHuLtrDr0DHTaotI1aaAJCLn75JboEEnyM+Bn54xtfQ1F4dzWdOa5BW4eH7GBlNri0jVpYAkIufPboc+r4HNDuu+hW2/mFbaZrMxtm9rHHYbc9bv55fNB02rLSJVlwKSiJSPum0h5m738swnwGneVa6bhVXnzs6RAIydtp68ApdptUWkalJAEpHyc+Uo8A+Fg6mw4gNTSz8adxE1A3zYcjCHyUu3m1pbRKoeBSQRKT/VQiFurHt5YQIc3W9a6WB/b566ugUAbyVu5kDWCdNqi0jVo4AkIuWr3R1Qrz3kZsHc0aaW7tehAW0jQsjOLeCl2RtNrS0iVYsCkoiUr5MTtrHB2i9hR5KJpW0819d9n7apq/eQvOOQabVFpGpRQBKR8le/A7Qf6F6e+QQ4C0wrHR0Rws0xDQAYM209Tpfu0yYi504BSUQqRvcx4BcC+3+D5I9NLf3k1S2o7ufFuj1ZfL1ql6m1RaRqUEASkYoRUBO6P+tenj8ecjJMK10r0JfH4poB8OqcNDKPmXfJARGpGhSQRKTidIiH8EvgRCbMG2Nq6Ts6N6JZWCCHcvL499w0U2uLiOdTQBKRimN3QJ/X3ctrPoPdq0wr7e2wM/Y694TtKct2kLovy7TaIuL5FJBEpGJFdILoAe7lGcPB5TSt9GVRtejdJhyX4Z6wbRiasC0ipaOAJCIVL24s+AbDvhRY/amppUf1aYWft50V2w7x49p9ptYWEc+lgCQiFS+wDlz5tHs58Tk4Zt71ieqH+DOoWxQAL85IJSfXvEsOiIjnUkASEXN0vBfqtIbjhyFxnKml7+/ahIhQf9KzTjBpwe+m1hYRz6SAJCLmcHhB71fdy8mfwN41ppX283bwbJ9WAPz3l21sz8gxrbaIeCbLA9KkSZOIjIzEz8+P2NhYVqxYUeLYqVOnEhMTQ0hICAEBAURHRzNlypRTxvTs2ZOaNWtis9lISUk5ZTvdunXDZrMVeTz44IPlvWsiUlxkF2hzM2DAjMfB5TKtdI9WYXRtVps8p4vx0zeYVldEPJOlAemrr75i2LBhjBkzhtWrV9O2bVt69erFgQMHTjs+NDSUUaNGkZSUxNq1a4mPjyc+Pp45c+YUjsnJyeHyyy/n5ZdfPmPt++67j3379hU+XnnllXLdNxEpQY9x4BMIe1bBr1+YVtZmszHmulZ42W0kbjzAgo2n//8ZEREAm2Hh915jY2Pp2LEjEydOBMDlchEREcGQIUMYMWJEqbbRvn17+vTpw/jx44us3759O40bN2bNmjVER0cXea5bt25ER0fz5ptvlrrX3NxccnNzC3/OysoiIiKCzMxMgoKCSr0dEQGWToCfnoFqtWDIKvCvYVrpF2em8p+ftxJZsxpzHuuKr5fDtNoiYr2srCyCg4PP+vlt2RGkvLw8kpOTiYuL+6sZu524uDiSks5+92/DMEhMTCQtLY2uXbuec/3PP/+cWrVqcfHFFzNy5EiOHTt2xvEJCQkEBwcXPiIiIs65poj8KfZBqNUcjmXAghdNLT3kqihqV/dl+x/H+HDxNlNri4jnsCwgZWRk4HQ6CQsLK7I+LCyM9PT0El+XmZlJYGAgPj4+9OnThwkTJtCjR49zqn3bbbfx2WefsWDBAkaOHMmUKVO4/fbbz/iakSNHkpmZWfjYtUs3wBQpM4f3XxO2V/4X0n8zrXR1P29GXtMCgInzfyc984RptUXEc3hZ3cC5ql69OikpKWRnZ5OYmMiwYcNo0qQJ3bp1K/U27r///sLlNm3aULduXbp3786WLVto2rTpaV/j6+uLr6/v+bYvIic1uQJa3wjrv4OZT0D8LLDZTCl9Y7v6fL58J8k7DvPizFTevrWdKXVFxHNYdgSpVq1aOBwO9u/fX2T9/v37CQ8PL/F1drudqKgooqOjGT58OP369SMhIeG8eomNjQXg9991fRQRU/V8Hryrwc4kWPu1aWVtNhvP9W2NzQbTft3L8q1/mFZbRDyDZQHJx8eHDh06kJiYWLjO5XKRmJhI586dS70dl8tVZPJ0WZy8FEDdunXPazsico6CG0DXJ9zLc5+FE+bdUPbi+sHc2qkh4L5PW4HTvEsOiEjlZ+nX/IcNG8YHH3zA5MmTSU1N5aGHHiInJ4f4+HgABg4cyMiRIwvHJyQkMHfuXLZu3Upqaiqvv/76KfOHDh06REpKChs2uK9zkpaWRkpKSuG8pi1btjB+/HiSk5PZvn0706ZNY+DAgXTt2pVLLrnExL0XEQA6PwyhTSF7Pyx8ydTST/RsTrC/NxvTj/K/FTtNrS0ilZulAal///689tprjB49mujoaFJSUpg9e3bhxO2dO3eyb99fN5fMyclh0KBBtG7dmi5duvDtt9/y2Wefce+99xaOmTZtGu3ataNPnz4A3HLLLbRr14733nsPcB+5mjdvHj179qRFixYMHz6cm266iR9//NHEPReRQl6+0PvP65Atfw8OpJpWukaAD4/3bAbAaz9t4lBOnmm1RaRys/Q6SJ6stNdREJFS+nIAbJwOkf+AO380bcJ2gdPFtRMWszH9KLfFNuTFG9uYUldErFHpr4MkIlJErxfByw+2/wLrp5pW1sth57m+rQH434qdrNuTaVptEam8FJBEpHKo0Qj+Mdy9POcZyM02rXRsk5r0bVsPw3BP2NaBdRFRQBKRyuOyR6BGJBzdCz+/amrpp3u3pJqPg+Qdh/luzR5Ta4tI5aOAJCKVh7cfXP3njaaTJkHGZtNKhwf7MfiqKAASZm0kO7fAtNoiUvloknYZaZK2SAX6oj9smg1NroQ7vjNtwnZugZOr3/yFbRk5NKkVQEg1bxx2G3abDYfdVmTZbrNht+Fetttw2P7+PH8bU3T9qWPd2znt+sJ17otbOk6z/u9j7X+Osds5zdg/+/nzuSLr7UVfZz/5vP3U7dlMei9EKkppP7897lYjInIBuDoBtiyArQsg9Udo1deUsr5eDkZf14r4j1eyNSPHlJqexmb7e4j6W9gqDFO2v4UpTlnvHvvX64qEur+HUVvR4Hb69X9ty3ZKiAUvux1vhw0vhx0vu839cPy5zm7Hy2HD22HHYbedss49/uTPJT/n5bDhbbdjtys4VjU6glRGOoIkUsHmvwA/vwLBEfDwCvCpZlrp33Znkp51AqfLwGUYxf4XXC4DZ7H1fy2DyzAKx/w1liJjDeNv608Ze/Z6LheF6866rT+3c7r17nV/PmcY6BOhbOw29zcivf8Mad6OPwNU8ZD293VnCmnFniu+7mTY8yr23MkeTluvhHWF4x22wvDn7ai6Rwt1BElEPNvlj8GvX0LmTlj8b7jqGdNKt2kQTBuCTatXmRgnQ5xRNIT9PXgVCVunDWR/hsFTxhR9ncv4K1SWtL3CgPj3MFo4lr/1VGy9y0WBy6DAaZDvcuE8uex0r893uihwGhT8fZzz5LKLfKe7VoHLvVzgdJH/53Ou04RIlwF5BS6q0qVGHX8edfMuFp5KH9L+dqStMJD9tew4JSgWfc7LYaPrRbUJD/azZP8VkESkcvKpBle/CF/dDkvegra3Qs2mVndV5dlsf36IWd1IJeZyGe4g9bfwVDR0/Rm8ThPInK5zC2l/f+7vIc0d/AycJfRw8rmi23Qv/72HkwHydEcOTx7tzC2w7j6Fk+/upIAkInKKFtdC0+6wJRFmj4DbvjZtwrZISex2Gz52Gz5V6IvgJ0OT82+hqXhgKyj2XJHAdjLEFTvi5iwMbmcff7qQWTPAx7LfiQKSiFReNhtc8wq8cyls/sn9zbbm11jdlUiV457g7rC6jUql6sRfEamaakXBZYPdy7Oegvzj1vYjIhcEBSQRqfz+8TgE1YcjO2DJ21Z3IyIXAAUkEan8fAOh5/Pu5cX/hsPbLW1HRKo+BSQR8Qytb4TGXaHgBMx+2upuRKSKU0ASEc9gs8E1r4LdC9JmwOa5VnckIlWYApKIeI46LSD2QffyrCehINfafkSkylJAEhHPcsVTEBgGh7ZC0kSruxGRKkoBSUQ8i1/QXxO2f34Njuyyth8RqZIUkETE87T5FzS8DPKPwU+jrO5GRKogBSQR8Tw2G/R+FWwO2PADbFlgdUciUsUoIImIZwq/GDrd516e9SQUVKX7qIuI1RSQRMRzdRsJAbUhYxMsf8/qbkSkClFAEhHP5R8Ccc+5lxe9DFn7LG1HRKoOBSQR8Wxtb4UGHSEvG356xupuRKSKUEASEc9mt0Pv1wAbrPsGti+2uiMRqQIUkETE89WLhpi73csznwBnvqXtiIjnU0ASkarhqmfAPxQObICV/7W6GxHxcApIIlI1VAuFuDHu5QUvwtH91vYjIh5NAUlEqo52d0C9dpCbBfPGWN2NiHgwBSQRqTrsDuj9OmCDX/8HO5dZ3ZGIeCgFJBGpWhp0gPZ3uJdnPg4up7X9iIhHUkASkaqn+xjwC4b032DVR1Z3IyIeSAFJRKqegFpw1bPu5fnjISfD2n5ExOMoIIlI1RRzN4S3gROZMG+s1d2IiIdRQBKRqqlwwjawZgrsTra2HxHxKApIIlJ1NYyFtre5l2cO14RtESk1ywPSpEmTiIyMxM/Pj9jYWFasWFHi2KlTpxITE0NISAgBAQFER0czZcqUU8b07NmTmjVrYrPZSElJOWU7J06c4OGHH6ZmzZoEBgZy0003sX+/LionUiX1eA58g2DvGveRJBGRUrA0IH311VcMGzaMMWPGsHr1atq2bUuvXr04cODAaceHhoYyatQokpKSWLt2LfHx8cTHxzNnzpzCMTk5OVx++eW8/PLLJdZ97LHH+PHHH/m///s/Fi1axN69e/nnP/9Z7vsnIpVAYB248mn38rzn4Ngha/sREY9gMwzDsKp4bGwsHTt2ZOLEiQC4XC4iIiIYMmQII0aMKNU22rdvT58+fRg/fnyR9du3b6dx48asWbOG6OjowvWZmZnUrl2bL774gn79+gGwceNGWrZsSVJSEpdeeulp6+Tm5pKbm1v4c1ZWFhEREWRmZhIUFHQuuy0iZnMWwPv/cN+nLeZuuPYNqzsSEYtkZWURHBx81s9vy44g5eXlkZycTFxc3F/N2O3ExcWRlJR01tcbhkFiYiJpaWl07dq11HWTk5PJz88vUrdFixY0bNjwjHUTEhIIDg4ufERERJS6pohYzOEFvV9zL6/6GPamWNqOiFR+lgWkjIwMnE4nYWFhRdaHhYWRnp5e4usyMzMJDAzEx8eHPn36MGHCBHr06FHquunp6fj4+BASEnJOdUeOHElmZmbhY9euXaWuKSKVQGQXaPMvwPjzCtsuqzsSkUrMy+oGzlX16tVJSUkhOzubxMREhg0bRpMmTejWrVuF1vX19cXX17dCa4hIBesxHtJmwe6V7nu1tRtgdUciUklZdgSpVq1aOByOU749tn//fsLDw0t8nd1uJyoqiujoaIYPH06/fv1ISEgodd3w8HDy8vI4cuTIOdUVkSogqC5c8ZR7ee5oOH7E0nZEpPKyLCD5+PjQoUMHEhMTC9e5XC4SExPp3LlzqbfjcrmKTJ4+mw4dOuDt7V2kblpaGjt37jynuiLioWIfhFrN4FgGLCz9P65E5MJi6Sm2YcOGceeddxITE0OnTp148803ycnJIT4+HoCBAwdSv379wiNECQkJxMTE0LRpU3Jzc5k5cyZTpkzh3XffLdzmoUOH2LlzJ3v37gXc4QfcR47Cw8MJDg7mnnvuYdiwYYSGhhIUFMSQIUPo3Llzid9gE5EqxMsHer8Kn14PK/4D7e6A8Iut7kpEKhlLA1L//v05ePAgo0ePJj09nejoaGbPnl04cXvnzp3Y7X8d5MrJyWHQoEHs3r0bf39/WrRowWeffUb//v0Lx0ybNq0wYAHccsstAIwZM4axY8cC8MYbb2C327npppvIzc2lV69evPPOOybssYhUCk26QasbYMP37gnb8bPAZrO4KRGpTCy9DpInK+11FESkksrcDRM7Qv4x+OcHcMnNVnckIiao9NdBEhGxVHAD6Pq4e/mnZ+BElrX9iEilooAkIheuzoMhtClk74dFJd+eSEQuPApIInLh8vKFa15xLy9/Dw5stLYfEak0FJBE5MJ2URy0uBZcBe4J25qWKSIoIImIQK8XwcsPtv8C67+zuhsRqQQUkEREajSCy4e5l+eMgtxsa/sREcspIImIAHR5BEIawdG98MtrVncjIhZTQBIRAfD2h2v+/Cbb0omwd437Xm252VCQCy6Xpe2JiLksvZK2iEil0vwauKgXbJ4D/+l26vM2O9i9weENdq8//9cbHF5F15/pucL13mB3nOG502yjcF1J2z+XXrzBrn8ji5REAUlE5O+ueRn2r4OsPac+Z7jAmet+VAVFAp/jzEHrnMPa39bbvc4SFItvo6xjHVb/RqUKUUASEfm70MYwbIP76/7OfPfX/1354Dz5v/mn+bngDGML/vaaYj+7Cs4w9nT1zrWXYrWKq2qBD1vR8GSzn/rA9refS1r+2zpOt/4Mrz3teNsZavz9NcWfL2tPthK2X3xsSdupiJ5K83s6TV8Btd2nvy2ggCQicjo2G3j5AD5Wd1I+DOPMAczlPEs4K00YLGtwLG0vZwl8GODMcz/yTf8NS0W4fSpEdbektAKSiMiFwPbn0RWHt9WdlI8iga/Y0bKT6wzDfZTsdA+MEp4vvs4o9prTba+kbRV/TUnLpenrDK8/r77O4XdQYh0XGJzj/peyjoWnTRWQRETE81S1wCeVjr7CICIiIlKMApKIiIhIMQpIIiIiIsUoIImIiIgUo4AkIiIiUowCkoiIiEgxCkgiIiIixSggiYiIiBSjgCQiIiJSjAKSiIiISDEKSCIiIiLFKCCJiIiIFKOAJCIiIlKMApKIiIhIMV5WN+CpDMMAICsry+JOREREpLROfm6f/BwviQJSGR09ehSAiIgIizsRERGRc3X06FGCg4NLfN5mnC1CyWm5XC727t1L9erVsdls5bbdrKwsIiIi2LVrF0FBQeW23cqkqu+j9s/zVfV9rOr7B1V/H7V/ZWcYBkePHqVevXrY7SXPNNIRpDKy2+00aNCgwrYfFBRUJf+j/7uqvo/aP89X1fexqu8fVP191P6VzZmOHJ2kSdoiIiIixSggiYiIiBSjgFTJ+Pr6MmbMGHx9fa1upcJU9X3U/nm+qr6PVX3/oOrvo/av4mmStoiIiEgxOoIkIiIiUowCkoiIiEgxCkgiIiIixSggiYiIiBSjgGSBSZMmERkZiZ+fH7GxsaxYseKM4//v//6PFi1a4OfnR5s2bZg5c6ZJnZbduezjJ598gs1mK/Lw8/Mzsdtz8/PPP3PddddRr149bDYb33///Vlfs3DhQtq3b4+vry9RUVF88sknFd5nWZ3r/i1cuPCU989ms5Genm5Ow+coISGBjh07Ur16derUqcMNN9xAWlraWV/nKX+HZdk/T/sbfPfdd7nkkksKLyLYuXNnZs2adcbXeMr7B+e+f572/hX30ksvYbPZGDp06BnHmf0eKiCZ7KuvvmLYsGGMGTOG1atX07ZtW3r16sWBAwdOO37p0qXceuut3HPPPaxZs4YbbriBG264gXXr1pnceemd6z6C+2qp+/btK3zs2LHDxI7PTU5ODm3btmXSpEmlGr9t2zb69OnDlVdeSUpKCkOHDuXee+9lzpw5Fdxp2Zzr/p2UlpZW5D2sU6dOBXV4fhYtWsTDDz/MsmXLmDt3Lvn5+fTs2ZOcnJwSX+NJf4dl2T/wrL/BBg0a8NJLL5GcnMyqVau46qqruP7661m/fv1px3vS+wfnvn/gWe/f361cuZL333+fSy655IzjLHkPDTFVp06djIcffrjwZ6fTadSrV89ISEg47fibb77Z6NOnT5F1sbGxxgMPPFChfZ6Pc93Hjz/+2AgODjapu/IFGN99990Zxzz55JNG69ati6zr37+/0atXrwrsrHyUZv8WLFhgAMbhw4dN6am8HThwwACMRYsWlTjGE/8OTyrN/nny3+BJNWrUMP773/+e9jlPfv9OOtP+eer7d/ToUeOiiy4y5s6da1xxxRXGo48+WuJYK95DHUEyUV5eHsnJycTFxRWus9vtxMXFkZSUdNrXJCUlFRkP0KtXrxLHW60s+wiQnZ1No0aNiIiIOOu/lDyNp72HZRUdHU3dunXp0aMHS5YssbqdUsvMzAQgNDS0xDGe/B6WZv/Ac/8GnU4nX375JTk5OXTu3Pm0Yzz5/SvN/oFnvn8PP/wwffr0OeW9OR0r3kMFJBNlZGTgdDoJCwsrsj4sLKzE+Rrp6ennNN5qZdnH5s2b89FHH/HDDz/w2Wef4XK5uOyyy9i9e7cZLVe4kt7DrKwsjh8/blFX5adu3bq89957fPvtt3z77bdERETQrVs3Vq9ebXVrZ+VyuRg6dChdunTh4osvLnGcp/0dnlTa/fPEv8HffvuNwMBAfH19efDBB/nuu+9o1arVacd64vt3Lvvnie/fl19+yerVq0lISCjVeCveQ68K27JIKXXu3LnIv4wuu+wyWrZsyfvvv8/48eMt7ExKo3nz5jRv3rzw58suu4wtW7bwxhtvMGXKFAs7O7uHH36YdevWsXjxYqtbqRCl3T9P/Bts3rw5KSkpZGZm8s0333DnnXeyaNGiEkOEpzmX/fO092/Xrl08+uijzJ07t1JPJldAMlGtWrVwOBzs37+/yPr9+/cTHh5+2teEh4ef03irlWUfi/P29qZdu3b8/vvvFdGi6Up6D4OCgvD397eoq4rVqVOnSh86Bg8ezPTp0/n5559p0KDBGcd62t8hnNv+FecJf4M+Pj5ERUUB0KFDB1auXMlbb73F+++/f8pYT3z/zmX/iqvs719ycjIHDhygffv2heucTic///wzEydOJDc3F4fDUeQ1VryHOsVmIh8fHzp06EBiYmLhOpfLRWJiYonnljt37lxkPMDcuXPPeC7aSmXZx+KcTie//fYbdevWrag2TeVp72F5SElJqbTvn2EYDB48mO+++4758+fTuHHjs77Gk97DsuxfcZ74N+hyucjNzT3tc570/pXkTPtXXGV//7p3785vv/1GSkpK4SMmJoYBAwaQkpJySjgCi97DCpv+Laf15ZdfGr6+vsYnn3xibNiwwbj//vuNkJAQIz093TAMw7jjjjuMESNGFI5fsmSJ4eXlZbz22mtGamqqMWbMGMPb29v47bffrNqFszrXfXzuueeMOXPmGFu2bDGSk5ONW265xfDz8zPWr19v1S6c0dGjR401a9YYa9asMQDj3//+t7FmzRpjx44dhmEYxogRI4w77rijcPzWrVuNatWqGU888YSRmppqTJo0yXA4HMbs2bOt2oUzOtf9e+ONN4zvv//e2Lx5s/Hbb78Zjz76qGG324158+ZZtQtn9NBDDxnBwcHGwoULjX379hU+jh07VjjGk/8Oy7J/nvY3OGLECGPRokXGtm3bjLVr1xojRowwbDab8dNPPxmG4dnvn2Gc+/552vt3OsW/xVYZ3kMFJAtMmDDBaNiwoeHj42N06tTJWLZsWeFzV1xxhXHnnXcWGf/1118bzZo1M3x8fIzWrVsbM2bMMLnjc3cu+zh06NDCsWFhYUbv3r2N1atXW9B16Zz8Wnvxx8l9uvPOO40rrrjilNdER0cbPj4+RpMmTYyPP/7Y9L5L61z37+WXXzaaNm1q+Pn5GaGhoUa3bt2M+fPnW9N8KZxu34Ai74kn/x2WZf887W/w7rvvNho1amT4+PgYtWvXNrp3714YHgzDs98/wzj3/fO09+90igekyvAe2gzDMCru+JSIiIiI59EcJBEREZFiFJBEREREilFAEhERESlGAUlERESkGAUkERERkWIUkERERESKUUASERERKUYBSURERKQYBSQRkXKwcOFCbDYbR44csboVESkHCkgiIiIixSggiYiIiBSjgCQiVYLL5SIhIYHGjRvj7+9P27Zt+eabb4C/Tn/NmDGDSy65BD8/Py699FLWrVtXZBvffvstrVu3xtfXl8jISF5//fUiz+fm5vLUU08RERGBr68vUVFRfPjhh0XGJCcnExMTQ7Vq1bjssstIS0ur2B0XkQqhgCQiVUJCQgKffvop7733HuvXr+exxx7j9ttvZ9GiRYVjnnjiCV5//XVWrlxJ7dq1ue6668jPzwfcwebmm2/mlltu4bfffmPs2LE8++yzfPLJJ4WvHzhwIP/73/94++23SU1N5f333ycwMLBIH6NGjeL1119n1apVeHl5cffdd5uy/yJSvmyGYRhWNyEicj5yc3MJDQ1l3rx5dO7cuXD9vffey7Fjx7j//vu58sor+fLLL+nfvz8Ahw4dokGDBnzyySfcfPPNDBgwgIMHD/LTTz8Vvv7JJ59kxowZrF+/nk2bNtG8eXPmzp1LXFzcKT0sXLiQK6+8knnz5tG9e3cAZs6cSZ8+fTh+/Dh+fn4V/FsQkfKkI0gi4vF+//13jh07Ro8ePQgMDCx8fPrpp2zZsqVw3N/DU2hoKM2bNyc1NRWA1NRUunTpUmS7Xbp0YfPmzTidTlJSUnA4HFxxxRVn7OWSSy4pXK5bty4ABw4cOO99FBFzeVndgIjI+crOzgZgxowZ1K9fv8hzvr6+RUJSWfn7+5dqnLe3d+GyzWYD3POjRMSz6AiSiHi8Vq1a4evry86dO4mKiiryiIiIKBy3bNmywuXDhw+zadMmWrZsCUDLli1ZsmRJke0uWbKEZs2a4XA4aNOmDS6Xq8icJhGpunQESUQ8XvXq1Xn88cd57LHHcLlcXH755WRmZrJkyRKCgoJo1KgRAOPGjaNmzZqEhYUxatQoatWqxQ033ADA8OHD6dixI+PHj6d///4kJSUxceJE3nnnHQAiIyO58847ufvuu3n77bdp27YtO3bs4MCBA9x8881W7bqIVBAFJBGpEsaPH0/t2rVJSEhg69athISE0L59e55++unCU1wvvfQSjz76KJs3byY6Opoff/wRHx8fANq3b8/XX3/N6NGjGT9+PHXr1mXcuHHcddddhTXeffddnn76aQYNGsQff/xBw4YNefrpp63YXRGpYPoWm4hUeSe/YXb48GFCQkKsbkdEPIDmIImIiIgUo4AkIiIiUoxOsYmIiIgUoyNIIiIiIsUoIImIiIgUo4AkIiIiUowCkoiIiEgxCkgiIiIixSggiYiIiBSjgCQiIiJSjAKSiIiISDH/Dwfz+IXZu1bTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 150037 TEST: 73758\n",
      "dataset built\n",
      "Loss in trainset: 0.3263\n",
      "Loss in testset: 0.3690\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "144b0e6f945c4509bde2a76cbf02e625",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/20733 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [2.2252, 2.3355, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0, 0.7462]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8404623b62e148ab949ce4657bc80f9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/387912 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3190\n",
      "Loss in testset: 0.3685\n",
      "iteration: 38656\n",
      "w: [2.3744, 2.4901, 5.5873, 2.0552, 1.3189, 0.0, 1.139, 0.0516, 0.7556, 1.6509, 0.6884, 0.8985, 1.3238, 0.5698]\n",
      "iteration: 77312\n",
      "w: [2.3744, 2.4901, 5.1834, 1.7915, 1.5154, 0.019, 1.1318, 0.014, 0.769, 1.7034, 0.6475, 0.8891, 1.6718, 0.5049]\n",
      "iteration: 115968\n",
      "w: [2.3744, 2.4901, 4.9896, 1.588, 1.808, 0.0332, 1.2296, 0.01, 0.9149, 1.7211, 0.6851, 0.7608, 1.9377, 0.478]\n",
      "Loss in trainset: 0.2998\n",
      "Loss in testset: 0.3495\n",
      "iteration: 154392\n",
      "w: [2.3744, 2.4901, 4.8128, 1.6961, 1.6493, 0.0306, 1.0719, 0.0155, 0.8061, 1.7894, 0.6416, 0.8603, 2.0, 0.4477]\n",
      "iteration: 193048\n",
      "w: [2.3744, 2.4901, 4.7131, 1.7535, 1.4022, 0.0642, 1.0675, 0.01, 0.8089, 1.7121, 0.726, 0.8684, 1.9608, 0.5794]\n",
      "iteration: 231704\n",
      "w: [2.3744, 2.4901, 4.7682, 1.6547, 1.7567, 0.0434, 1.0618, 0.0197, 0.8104, 1.7274, 0.7114, 0.82, 1.9905, 0.5589]\n",
      "Loss in trainset: 0.2984\n",
      "Loss in testset: 0.3477\n",
      "iteration: 270128\n",
      "w: [2.3744, 2.4901, 4.7462, 1.6425, 1.7112, 0.0277, 0.9963, 0.01, 0.7451, 1.7017, 0.7342, 0.7517, 1.9579, 0.6199]\n",
      "iteration: 308784\n",
      "w: [2.3744, 2.4901, 4.6836, 1.5939, 1.7136, 0.015, 1.0213, 0.01, 0.7632, 1.7352, 0.6971, 0.8315, 1.9598, 0.5461]\n",
      "iteration: 347440\n",
      "w: [2.3744, 2.4901, 4.6595, 1.5931, 1.696, 0.0085, 1.0342, 0.0101, 0.7743, 1.7314, 0.7002, 0.8442, 1.9624, 0.5308]\n",
      "iteration: 386096\n",
      "w: [2.3744, 2.4901, 4.6618, 1.5976, 1.6959, 0.0063, 1.0327, 0.01, 0.7725, 1.7317, 0.6996, 0.8408, 1.9638, 0.5421]\n",
      "Loss in trainset: 0.2979\n",
      "Loss in testset: 0.3469\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=3)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=3, lr=4e-2, batch_size=256)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.1498, 2.5453, 4.3085, 1.1752, 1.5978, 0.0025, 1.0239, 0.0123, 0.8106, 1.7192, 0.6621, 0.8055, 1.7098, 0.5478]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,1,2,4,9,17,34,68,135,265,521\n",
      "difficulty history: 0,6.7,6.7,6.6,6.6,6.6,6.6,6.6,6.6,6.6,6.6\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,4,9,20,45,100,223,495,1092,2396,5234\n",
      "difficulty history: 0,5.5,5.5,5.5,5.5,5.5,5.5,5.5,5.5,5.5,5.5\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,6,16,40,100,248,613,1503,3664,8875,21358\n",
      "difficulty history: 0,4.3,4.3,4.3,4.3,4.3,4.3,4.3,4.3,4.3,4.3\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,9,25,69,190,519,1406,3779,10078,26668,36500\n",
      "difficulty history: 0,3.1,3.1,3.1,3.1,3.1,3.1,3.2,3.2,3.2,3.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "        stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "        return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = next_interval(requestRetention, float(states[0]), avg_w[13])\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(f\"interval history: {t_history}\")\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([6.2404, 4.3085])\n",
      "tensor([15.6361,  4.3085])\n",
      "tensor([39.7112,  4.3085])\n",
      "tensor([99.6452,  4.3085])\n",
      "tensor([248.0456,   4.3085])\n",
      "tensor([45.6185,  7.4961])\n",
      "tensor([ 9.6437, 10.0000])\n",
      "tensor([11.9240,  9.9858])\n",
      "tensor([14.7297,  9.9716])\n",
      "tensor([18.2567,  9.9574])\n",
      "tensor([22.5992,  9.9433])\n",
      "tensor([28.1260,  9.9292])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,6,16,40,100,248,46,10,12,15,18,23,28\n",
      "difficulty history: 0,4.3,4.3,4.3,4.3,4.3,7.5,10.0,10.0,10.0,10.0,9.9,9.9\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = next_interval(requestRetention, float(states[0]), avg_w[13])\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.015197\n",
      "2     0.034402\n",
      "3     0.156925\n",
      "4     0.132295\n",
      "5     0.040104\n",
      "6     0.055806\n",
      "7     0.116531\n",
      "8     0.065904\n",
      "9     0.024554\n",
      "10    0.358283\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(10)\n",
    "for i in range(10):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  100.18\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53971911a6e74f919b8b772d04cfa1d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.78-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "index_len = 664\n",
    "index_offset = 200\n",
    "d_range = 10\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 200000\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return int(round(np.log(stability) / np.log(base)) + index_offset)\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + 2) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * (11 - d) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * np.power(s, avg_w[11]) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2)):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        while diff > 0.1:\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            for s_index in range(index_len - 2, -1, -1):\n",
    "                stability = stability_list[s_index]\n",
    "                interval = next_interval(recall, stability, avg_w[13])\n",
    "                p_recall = forgetting_curve(stability, interval, avg_w[13])\n",
    "                recall_s = cal_next_recall_stability(stability, p_recall, d, 1)\n",
    "                forget_d = min(d + d_offset, 10)\n",
    "                forget_s = cal_next_recall_stability(stability, p_recall, forget_d, 0)\n",
    "                recall_s_index = min(stability2index(recall_s), index_len - 1)\n",
    "                forget_s_index = min(max(stability2index(forget_s), 0), index_len - 1)\n",
    "                recall_time = time_list[d - 1][recall_s_index] + r_time\n",
    "                forget_time = time_list[forget_d - 1][forget_s_index] + f_time\n",
    "                exp_time = p_recall * recall_time + (1.0 - p_recall) * forget_time\n",
    "                if exp_time < time_list[d - 1][s_index]:\n",
    "                    time_list[d - 1][s_index] = exp_time\n",
    "            diff = s0_time - time_list[d - 1][s0_index]\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(10)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3403\n",
      "Loss after training: 0.3124\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = forgetting_curve(dataset['stability'], dataset['delta_t'], init_w[13])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = forgetting_curve(dataset['stability'], dataset['delta_t'], avg_w[13])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9708\n",
      "RMSE: 0.0114\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = np.sqrt(mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts))\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration')\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration')\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\n",
       "#T_3a030_row5_col7 {\n",
       "  background-color: #ff2d2d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row6_col6, #T_3a030_row10_col7 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row6_col7, #T_3a030_row9_col6, #T_3a030_row12_col5, #T_3a030_row19_col2 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row6_col9, #T_3a030_row20_col3 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row7_col5, #T_3a030_row16_col5, #T_3a030_row18_col5 {\n",
       "  background-color: #ffb5b5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row7_col6 {\n",
       "  background-color: #8989ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row7_col7, #T_3a030_row12_col6, #T_3a030_row16_col7 {\n",
       "  background-color: #ffb9b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row7_col9, #T_3a030_row9_col7, #T_3a030_row10_col8, #T_3a030_row10_col9 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row8_col6, #T_3a030_row13_col5 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row8_col7, #T_3a030_row9_col9, #T_3a030_row11_col9, #T_3a030_row13_col4, #T_3a030_row17_col3 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row9_col3, #T_3a030_row17_col2, #T_3a030_row18_col3 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row9_col4, #T_3a030_row11_col7 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row9_col8, #T_3a030_row10_col6, #T_3a030_row11_col5, #T_3a030_row15_col8 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row10_col2, #T_3a030_row17_col1 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row10_col3, #T_3a030_row15_col2 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row11_col3 {\n",
       "  background-color: #a9a9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row11_col4 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row11_col8, #T_3a030_row17_col6 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row12_col1 {\n",
       "  background-color: #9d9dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row12_col2 {\n",
       "  background-color: #9191ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row12_col3 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row12_col4, #T_3a030_row13_col3, #T_3a030_row13_col9, #T_3a030_row16_col4 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row12_col8, #T_3a030_row14_col5, #T_3a030_row16_col6, #T_3a030_row17_col4 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row12_col9, #T_3a030_row14_col9, #T_3a030_row15_col9, #T_3a030_row16_col3 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row13_col1 {\n",
       "  background-color: #8181ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row13_col2, #T_3a030_row21_col2 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row13_col6 {\n",
       "  background-color: #ffadad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row13_col7 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row13_col8 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row14_col2 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row14_col3, #T_3a030_row16_col2 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row14_col4 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row14_col6, #T_3a030_row17_col5 {\n",
       "  background-color: #ff9d9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row15_col0 {\n",
       "  background-color: #7171ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row15_col1 {\n",
       "  background-color: #8d8dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row15_col6 {\n",
       "  background-color: #ff9595;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row16_col1 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row16_col9 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row18_col0 {\n",
       "  background-color: #8585ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row18_col1 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row18_col2 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row18_col4 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row19_col0 {\n",
       "  background-color: #7575ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3a030_row19_col3 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row19_col4 {\n",
       "  background-color: #ffa9a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a030_row19_col5 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3a030_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th class=\"col_heading level0 col0\" >1</th>\n",
       "      <th class=\"col_heading level0 col1\" >2</th>\n",
       "      <th class=\"col_heading level0 col2\" >3</th>\n",
       "      <th class=\"col_heading level0 col3\" >4</th>\n",
       "      <th class=\"col_heading level0 col4\" >5</th>\n",
       "      <th class=\"col_heading level0 col5\" >6</th>\n",
       "      <th class=\"col_heading level0 col6\" >7</th>\n",
       "      <th class=\"col_heading level0 col7\" >8</th>\n",
       "      <th class=\"col_heading level0 col8\" >9</th>\n",
       "      <th class=\"col_heading level0 col9\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row0\" class=\"row_heading level0 row0\" >0.26</th>\n",
       "      <td id=\"T_3a030_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_3a030_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_3a030_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_3a030_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_3a030_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_3a030_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_3a030_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_3a030_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_3a030_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_3a030_row0_col9\" class=\"data row0 col9\" >2.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row1\" class=\"row_heading level0 row1\" >0.36</th>\n",
       "      <td id=\"T_3a030_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_3a030_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_3a030_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_3a030_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_3a030_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_3a030_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_3a030_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_3a030_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_3a030_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_3a030_row1_col9\" class=\"data row1 col9\" >3.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row2\" class=\"row_heading level0 row2\" >0.51</th>\n",
       "      <td id=\"T_3a030_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_3a030_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_3a030_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_3a030_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_3a030_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_3a030_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_3a030_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_3a030_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_3a030_row2_col8\" class=\"data row2 col8\" ></td>\n",
       "      <td id=\"T_3a030_row2_col9\" class=\"data row2 col9\" >0.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row3\" class=\"row_heading level0 row3\" >0.71</th>\n",
       "      <td id=\"T_3a030_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_3a030_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_3a030_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_3a030_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_3a030_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_3a030_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_3a030_row3_col6\" class=\"data row3 col6\" ></td>\n",
       "      <td id=\"T_3a030_row3_col7\" class=\"data row3 col7\" ></td>\n",
       "      <td id=\"T_3a030_row3_col8\" class=\"data row3 col8\" ></td>\n",
       "      <td id=\"T_3a030_row3_col9\" class=\"data row3 col9\" >-1.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row4\" class=\"row_heading level0 row4\" >1.0</th>\n",
       "      <td id=\"T_3a030_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_3a030_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_3a030_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_3a030_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_3a030_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_3a030_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_3a030_row4_col6\" class=\"data row4 col6\" >9.43%</td>\n",
       "      <td id=\"T_3a030_row4_col7\" class=\"data row4 col7\" ></td>\n",
       "      <td id=\"T_3a030_row4_col8\" class=\"data row4 col8\" ></td>\n",
       "      <td id=\"T_3a030_row4_col9\" class=\"data row4 col9\" >-1.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row5\" class=\"row_heading level0 row5\" >1.4</th>\n",
       "      <td id=\"T_3a030_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_3a030_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_3a030_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_3a030_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_3a030_row5_col4\" class=\"data row5 col4\" ></td>\n",
       "      <td id=\"T_3a030_row5_col5\" class=\"data row5 col5\" ></td>\n",
       "      <td id=\"T_3a030_row5_col6\" class=\"data row5 col6\" ></td>\n",
       "      <td id=\"T_3a030_row5_col7\" class=\"data row5 col7\" >8.26%</td>\n",
       "      <td id=\"T_3a030_row5_col8\" class=\"data row5 col8\" ></td>\n",
       "      <td id=\"T_3a030_row5_col9\" class=\"data row5 col9\" >-1.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row6\" class=\"row_heading level0 row6\" >1.96</th>\n",
       "      <td id=\"T_3a030_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_3a030_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_3a030_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_3a030_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "      <td id=\"T_3a030_row6_col4\" class=\"data row6 col4\" ></td>\n",
       "      <td id=\"T_3a030_row6_col5\" class=\"data row6 col5\" ></td>\n",
       "      <td id=\"T_3a030_row6_col6\" class=\"data row6 col6\" >1.70%</td>\n",
       "      <td id=\"T_3a030_row6_col7\" class=\"data row6 col7\" >-0.11%</td>\n",
       "      <td id=\"T_3a030_row6_col8\" class=\"data row6 col8\" ></td>\n",
       "      <td id=\"T_3a030_row6_col9\" class=\"data row6 col9\" >-1.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row7\" class=\"row_heading level0 row7\" >2.74</th>\n",
       "      <td id=\"T_3a030_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_3a030_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_3a030_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_3a030_row7_col3\" class=\"data row7 col3\" ></td>\n",
       "      <td id=\"T_3a030_row7_col4\" class=\"data row7 col4\" ></td>\n",
       "      <td id=\"T_3a030_row7_col5\" class=\"data row7 col5\" >2.92%</td>\n",
       "      <td id=\"T_3a030_row7_col6\" class=\"data row7 col6\" >-4.63%</td>\n",
       "      <td id=\"T_3a030_row7_col7\" class=\"data row7 col7\" >2.67%</td>\n",
       "      <td id=\"T_3a030_row7_col8\" class=\"data row7 col8\" ></td>\n",
       "      <td id=\"T_3a030_row7_col9\" class=\"data row7 col9\" >-0.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row8\" class=\"row_heading level0 row8\" >3.84</th>\n",
       "      <td id=\"T_3a030_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_3a030_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_3a030_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_3a030_row8_col3\" class=\"data row8 col3\" ></td>\n",
       "      <td id=\"T_3a030_row8_col4\" class=\"data row8 col4\" ></td>\n",
       "      <td id=\"T_3a030_row8_col5\" class=\"data row8 col5\" ></td>\n",
       "      <td id=\"T_3a030_row8_col6\" class=\"data row8 col6\" >0.52%</td>\n",
       "      <td id=\"T_3a030_row8_col7\" class=\"data row8 col7\" >0.28%</td>\n",
       "      <td id=\"T_3a030_row8_col8\" class=\"data row8 col8\" ></td>\n",
       "      <td id=\"T_3a030_row8_col9\" class=\"data row8 col9\" >1.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row9\" class=\"row_heading level0 row9\" >5.38</th>\n",
       "      <td id=\"T_3a030_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_3a030_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_3a030_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "      <td id=\"T_3a030_row9_col3\" class=\"data row9 col3\" >-1.07%</td>\n",
       "      <td id=\"T_3a030_row9_col4\" class=\"data row9 col4\" >0.81%</td>\n",
       "      <td id=\"T_3a030_row9_col5\" class=\"data row9 col5\" ></td>\n",
       "      <td id=\"T_3a030_row9_col6\" class=\"data row9 col6\" >-0.06%</td>\n",
       "      <td id=\"T_3a030_row9_col7\" class=\"data row9 col7\" >-0.45%</td>\n",
       "      <td id=\"T_3a030_row9_col8\" class=\"data row9 col8\" >-0.49%</td>\n",
       "      <td id=\"T_3a030_row9_col9\" class=\"data row9 col9\" >0.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row10\" class=\"row_heading level0 row10\" >7.53</th>\n",
       "      <td id=\"T_3a030_row10_col0\" class=\"data row10 col0\" ></td>\n",
       "      <td id=\"T_3a030_row10_col1\" class=\"data row10 col1\" ></td>\n",
       "      <td id=\"T_3a030_row10_col2\" class=\"data row10 col2\" >-2.97%</td>\n",
       "      <td id=\"T_3a030_row10_col3\" class=\"data row10 col3\" >-2.43%</td>\n",
       "      <td id=\"T_3a030_row10_col4\" class=\"data row10 col4\" ></td>\n",
       "      <td id=\"T_3a030_row10_col5\" class=\"data row10 col5\" >0.99%</td>\n",
       "      <td id=\"T_3a030_row10_col6\" class=\"data row10 col6\" >-0.59%</td>\n",
       "      <td id=\"T_3a030_row10_col7\" class=\"data row10 col7\" >1.70%</td>\n",
       "      <td id=\"T_3a030_row10_col8\" class=\"data row10 col8\" >-0.36%</td>\n",
       "      <td id=\"T_3a030_row10_col9\" class=\"data row10 col9\" >-0.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row11\" class=\"row_heading level0 row11\" >10.54</th>\n",
       "      <td id=\"T_3a030_row11_col0\" class=\"data row11 col0\" ></td>\n",
       "      <td id=\"T_3a030_row11_col1\" class=\"data row11 col1\" ></td>\n",
       "      <td id=\"T_3a030_row11_col2\" class=\"data row11 col2\" >-1.84%</td>\n",
       "      <td id=\"T_3a030_row11_col3\" class=\"data row11 col3\" >-3.42%</td>\n",
       "      <td id=\"T_3a030_row11_col4\" class=\"data row11 col4\" >2.26%</td>\n",
       "      <td id=\"T_3a030_row11_col5\" class=\"data row11 col5\" >-0.48%</td>\n",
       "      <td id=\"T_3a030_row11_col6\" class=\"data row11 col6\" >2.46%</td>\n",
       "      <td id=\"T_3a030_row11_col7\" class=\"data row11 col7\" >0.79%</td>\n",
       "      <td id=\"T_3a030_row11_col8\" class=\"data row11 col8\" >2.09%</td>\n",
       "      <td id=\"T_3a030_row11_col9\" class=\"data row11 col9\" >0.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row12\" class=\"row_heading level0 row12\" >14.76</th>\n",
       "      <td id=\"T_3a030_row12_col0\" class=\"data row12 col0\" ></td>\n",
       "      <td id=\"T_3a030_row12_col1\" class=\"data row12 col1\" >-3.86%</td>\n",
       "      <td id=\"T_3a030_row12_col2\" class=\"data row12 col2\" >-4.25%</td>\n",
       "      <td id=\"T_3a030_row12_col3\" class=\"data row12 col3\" >-1.91%</td>\n",
       "      <td id=\"T_3a030_row12_col4\" class=\"data row12 col4\" >1.13%</td>\n",
       "      <td id=\"T_3a030_row12_col5\" class=\"data row12 col5\" >-0.03%</td>\n",
       "      <td id=\"T_3a030_row12_col6\" class=\"data row12 col6\" >2.79%</td>\n",
       "      <td id=\"T_3a030_row12_col7\" class=\"data row12 col7\" >3.72%</td>\n",
       "      <td id=\"T_3a030_row12_col8\" class=\"data row12 col8\" >1.91%</td>\n",
       "      <td id=\"T_3a030_row12_col9\" class=\"data row12 col9\" >0.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row13\" class=\"row_heading level0 row13\" >20.66</th>\n",
       "      <td id=\"T_3a030_row13_col0\" class=\"data row13 col0\" ></td>\n",
       "      <td id=\"T_3a030_row13_col1\" class=\"data row13 col1\" >-4.91%</td>\n",
       "      <td id=\"T_3a030_row13_col2\" class=\"data row13 col2\" >-1.18%</td>\n",
       "      <td id=\"T_3a030_row13_col3\" class=\"data row13 col3\" >1.21%</td>\n",
       "      <td id=\"T_3a030_row13_col4\" class=\"data row13 col4\" >0.22%</td>\n",
       "      <td id=\"T_3a030_row13_col5\" class=\"data row13 col5\" >0.57%</td>\n",
       "      <td id=\"T_3a030_row13_col6\" class=\"data row13 col6\" >3.21%</td>\n",
       "      <td id=\"T_3a030_row13_col7\" class=\"data row13 col7\" >1.37%</td>\n",
       "      <td id=\"T_3a030_row13_col8\" class=\"data row13 col8\" >3.07%</td>\n",
       "      <td id=\"T_3a030_row13_col9\" class=\"data row13 col9\" >1.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row14\" class=\"row_heading level0 row14\" >28.93</th>\n",
       "      <td id=\"T_3a030_row14_col0\" class=\"data row14 col0\" ></td>\n",
       "      <td id=\"T_3a030_row14_col1\" class=\"data row14 col1\" ></td>\n",
       "      <td id=\"T_3a030_row14_col2\" class=\"data row14 col2\" >-2.69%</td>\n",
       "      <td id=\"T_3a030_row14_col3\" class=\"data row14 col3\" >-1.53%</td>\n",
       "      <td id=\"T_3a030_row14_col4\" class=\"data row14 col4\" >0.31%</td>\n",
       "      <td id=\"T_3a030_row14_col5\" class=\"data row14 col5\" >1.90%</td>\n",
       "      <td id=\"T_3a030_row14_col6\" class=\"data row14 col6\" >3.86%</td>\n",
       "      <td id=\"T_3a030_row14_col7\" class=\"data row14 col7\" >2.38%</td>\n",
       "      <td id=\"T_3a030_row14_col8\" class=\"data row14 col8\" >3.75%</td>\n",
       "      <td id=\"T_3a030_row14_col9\" class=\"data row14 col9\" >0.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row15\" class=\"row_heading level0 row15\" >40.5</th>\n",
       "      <td id=\"T_3a030_row15_col0\" class=\"data row15 col0\" >-5.54%</td>\n",
       "      <td id=\"T_3a030_row15_col1\" class=\"data row15 col1\" >-4.42%</td>\n",
       "      <td id=\"T_3a030_row15_col2\" class=\"data row15 col2\" >-2.46%</td>\n",
       "      <td id=\"T_3a030_row15_col3\" class=\"data row15 col3\" >0.96%</td>\n",
       "      <td id=\"T_3a030_row15_col4\" class=\"data row15 col4\" >1.06%</td>\n",
       "      <td id=\"T_3a030_row15_col5\" class=\"data row15 col5\" >2.42%</td>\n",
       "      <td id=\"T_3a030_row15_col6\" class=\"data row15 col6\" >4.21%</td>\n",
       "      <td id=\"T_3a030_row15_col7\" class=\"data row15 col7\" >2.49%</td>\n",
       "      <td id=\"T_3a030_row15_col8\" class=\"data row15 col8\" >-0.52%</td>\n",
       "      <td id=\"T_3a030_row15_col9\" class=\"data row15 col9\" >0.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row16\" class=\"row_heading level0 row16\" >56.69</th>\n",
       "      <td id=\"T_3a030_row16_col0\" class=\"data row16 col0\" >-1.85%</td>\n",
       "      <td id=\"T_3a030_row16_col1\" class=\"data row16 col1\" >-0.68%</td>\n",
       "      <td id=\"T_3a030_row16_col2\" class=\"data row16 col2\" >-1.45%</td>\n",
       "      <td id=\"T_3a030_row16_col3\" class=\"data row16 col3\" >0.70%</td>\n",
       "      <td id=\"T_3a030_row16_col4\" class=\"data row16 col4\" >1.11%</td>\n",
       "      <td id=\"T_3a030_row16_col5\" class=\"data row16 col5\" >2.81%</td>\n",
       "      <td id=\"T_3a030_row16_col6\" class=\"data row16 col6\" >1.88%</td>\n",
       "      <td id=\"T_3a030_row16_col7\" class=\"data row16 col7\" >2.69%</td>\n",
       "      <td id=\"T_3a030_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_3a030_row16_col9\" class=\"data row16 col9\" >0.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row17\" class=\"row_heading level0 row17\" >79.37</th>\n",
       "      <td id=\"T_3a030_row17_col0\" class=\"data row17 col0\" ></td>\n",
       "      <td id=\"T_3a030_row17_col1\" class=\"data row17 col1\" >-3.05%</td>\n",
       "      <td id=\"T_3a030_row17_col2\" class=\"data row17 col2\" >-0.98%</td>\n",
       "      <td id=\"T_3a030_row17_col3\" class=\"data row17 col3\" >0.21%</td>\n",
       "      <td id=\"T_3a030_row17_col4\" class=\"data row17 col4\" >1.89%</td>\n",
       "      <td id=\"T_3a030_row17_col5\" class=\"data row17 col5\" >3.77%</td>\n",
       "      <td id=\"T_3a030_row17_col6\" class=\"data row17 col6\" >2.12%</td>\n",
       "      <td id=\"T_3a030_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_3a030_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "      <td id=\"T_3a030_row17_col9\" class=\"data row17 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row18\" class=\"row_heading level0 row18\" >111.12</th>\n",
       "      <td id=\"T_3a030_row18_col0\" class=\"data row18 col0\" >-4.71%</td>\n",
       "      <td id=\"T_3a030_row18_col1\" class=\"data row18 col1\" >-2.89%</td>\n",
       "      <td id=\"T_3a030_row18_col2\" class=\"data row18 col2\" >-0.20%</td>\n",
       "      <td id=\"T_3a030_row18_col3\" class=\"data row18 col3\" >-1.07%</td>\n",
       "      <td id=\"T_3a030_row18_col4\" class=\"data row18 col4\" >1.74%</td>\n",
       "      <td id=\"T_3a030_row18_col5\" class=\"data row18 col5\" >2.96%</td>\n",
       "      <td id=\"T_3a030_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_3a030_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "      <td id=\"T_3a030_row18_col8\" class=\"data row18 col8\" ></td>\n",
       "      <td id=\"T_3a030_row18_col9\" class=\"data row18 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row19\" class=\"row_heading level0 row19\" >155.57</th>\n",
       "      <td id=\"T_3a030_row19_col0\" class=\"data row19 col0\" >-5.36%</td>\n",
       "      <td id=\"T_3a030_row19_col1\" class=\"data row19 col1\" ></td>\n",
       "      <td id=\"T_3a030_row19_col2\" class=\"data row19 col2\" >-0.11%</td>\n",
       "      <td id=\"T_3a030_row19_col3\" class=\"data row19 col3\" >-2.23%</td>\n",
       "      <td id=\"T_3a030_row19_col4\" class=\"data row19 col4\" >3.33%</td>\n",
       "      <td id=\"T_3a030_row19_col5\" class=\"data row19 col5\" >2.58%</td>\n",
       "      <td id=\"T_3a030_row19_col6\" class=\"data row19 col6\" ></td>\n",
       "      <td id=\"T_3a030_row19_col7\" class=\"data row19 col7\" ></td>\n",
       "      <td id=\"T_3a030_row19_col8\" class=\"data row19 col8\" ></td>\n",
       "      <td id=\"T_3a030_row19_col9\" class=\"data row19 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row20\" class=\"row_heading level0 row20\" >217.8</th>\n",
       "      <td id=\"T_3a030_row20_col0\" class=\"data row20 col0\" ></td>\n",
       "      <td id=\"T_3a030_row20_col1\" class=\"data row20 col1\" ></td>\n",
       "      <td id=\"T_3a030_row20_col2\" class=\"data row20 col2\" >-1.79%</td>\n",
       "      <td id=\"T_3a030_row20_col3\" class=\"data row20 col3\" >-1.28%</td>\n",
       "      <td id=\"T_3a030_row20_col4\" class=\"data row20 col4\" ></td>\n",
       "      <td id=\"T_3a030_row20_col5\" class=\"data row20 col5\" ></td>\n",
       "      <td id=\"T_3a030_row20_col6\" class=\"data row20 col6\" ></td>\n",
       "      <td id=\"T_3a030_row20_col7\" class=\"data row20 col7\" ></td>\n",
       "      <td id=\"T_3a030_row20_col8\" class=\"data row20 col8\" ></td>\n",
       "      <td id=\"T_3a030_row20_col9\" class=\"data row20 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a030_level0_row21\" class=\"row_heading level0 row21\" >304.91</th>\n",
       "      <td id=\"T_3a030_row21_col0\" class=\"data row21 col0\" ></td>\n",
       "      <td id=\"T_3a030_row21_col1\" class=\"data row21 col1\" ></td>\n",
       "      <td id=\"T_3a030_row21_col2\" class=\"data row21 col2\" >-1.10%</td>\n",
       "      <td id=\"T_3a030_row21_col3\" class=\"data row21 col3\" ></td>\n",
       "      <td id=\"T_3a030_row21_col4\" class=\"data row21 col4\" ></td>\n",
       "      <td id=\"T_3a030_row21_col5\" class=\"data row21 col5\" ></td>\n",
       "      <td id=\"T_3a030_row21_col6\" class=\"data row21 col6\" ></td>\n",
       "      <td id=\"T_3a030_row21_col7\" class=\"data row21 col7\" ></td>\n",
       "      <td id=\"T_3a030_row21_col8\" class=\"data row21 col8\" ></td>\n",
       "      <td id=\"T_3a030_row21_col9\" class=\"data row21 col9\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x161e8c550>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='d_bin'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_W_Metric_raw.groupby(by=['d_bin']).agg('count').reset_index().plot.bar(x='d_bin', y='p', legend=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
